10.1016/j.sigpro.2013.12.026
Crossref journal-article
Elsevier BV
Signal Processing (78)
Bibliography

Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215–249.

Authors 4
  1. Marco A.F. Pimentel (first)
  2. David A. Clifton (additional)
  3. Lei Clifton (additional)
  4. Lionel Tarassenko (additional)
References 310 Referenced 1,225
  1. L. Tarassenko, P. Hayton, N. Cerneaz, M. Brady, Novelty detection for the identification of masses in mammograms, in: Proceedings of the 4th International Conference on Artificial Neural Networks, IET, 1995, pp. 442–447. (10.1049/cp:19950597)
  2. 10.1007/978-3-540-72847-4_1 / Pattern Recognit. Image Anal. / Known unknowns by Quinn (2007)
  3. L. Clifton, D. Clifton, P. Watkinson, L. Tarassenko, Identification of patient deterioration in vital-sign data using one-class support vector machines, in: Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS), IEEE, 2011, pp. 125–131.
  4. {'year': '2009', 'series-title': 'Novelty Detection', 'author': 'Tarassenko', 'key': '10.1016/j.sigpro.2013.12.026_bib4'} / Novelty Detection by Tarassenko (2009)
  5. 10.1016/j.ymssp.2009.09.009 / Mech. Syst. Signal Process. / Novelty detection in a changing environment by Surace (2010)
  6. 10.1016/j.comnet.2007.02.001 / Comput. Netw. / An overview of anomaly detection techniques by Patcha (2007)
  7. {'issue': '7', 'key': '10.1016/j.sigpro.2013.12.026_bib7', 'first-page': '26', 'article-title': 'A review of anomaly based intrusion detection systems', 'volume': '28', 'author': 'Jyothsna', 'year': '2011', 'journal-title': 'Int. J. Comput. Appl.'} / Int. J. Comput. Appl. / A review of anomaly based intrusion detection systems by Jyothsna (2011)
  8. C. Diehl, J. Hampshire, Real-time object classification and novelty detection for collaborative video surveillance, in: Proceedings of the International Joint Conference on Neural Networks, IJCNN'02, 2002, vol. 3, pp. 2620–2625. (10.1109/IJCNN.2002.1007557)
  9. 10.1109/TPAMI.2006.196 / IEEE Trans. Pattern Anal. Mach. Intell. / A neural network-based novelty detector for image sequence analysis by Markou (2006)
  10. 10.1007/s10846-007-9146-9 / J. Intell. Robotic Syst. / Real-time automated visual inspection using mobile robots by Vieira Neto (2007)
  11. 10.1002/rob.20396 / J. Field Robot. / Anytime online novelty and change detection for mobile robots by Sofman (2011)
  12. 10.1109/SURV.2010.021510.00088 / IEEE Commun. Surv. Tutor. / Outlier detection techniques for wireless sensor networks by Zhang (2010)
  13. H. Dutta, C. Giannella, K. Borne, H. Kargupta, Distributed top-k outlier detection from astronomy catalogs using the DEMAC system, in: Proceedings of the 7th SIAM International Conference on Data Mining, IEEE, 2007. (10.1137/1.9781611972771.47)
  14. H. Escalante, A comparison of outlier detection algorithms for machine learning, in: Proceedings of the International Conference on Communications in Computing, Citeseer, 2005.
  15. S. Basu, M. Bilenko, R. Mooney, A probabilistic framework for semi-supervised clustering, in: Proceedings of the 10th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), ACM, 2004, pp. 59–68. (10.1145/1014052.1014062)
  16. {'year': '2012', 'series-title': 'Bayesian Reasoning and Machine Learning', 'author': 'Barber', 'key': '10.1016/j.sigpro.2013.12.026_bib16'} / Bayesian Reasoning and Machine Learning by Barber (2012)
  17. C. Sammut, G. Webb, Encyclopedia of Machine Learning. Springer, 2011. Springer reference. (10.1007/978-0-387-30164-8)
  18. M. Moya, M. Koch, L. Hostetler, One-class classifier networks for target recognition applications, in: Proceedings of the World Congress on Neural Networks, International Neural Network Society, 1993, pp. 797–801.
  19. 10.1109/TKDE.2008.239 / IEEE Trans. Knowl. Data Eng. / Learning from imbalanced data by He (2009)
  20. {'key': '10.1016/j.sigpro.2013.12.026_bib20', 'first-page': '21', 'article-title': 'The novelty detection approach for different degrees of class imbalance', 'volume': 'vol. 4233', 'author': 'Lee', 'year': '2006'} / The novelty detection approach for different degrees of class imbalance by Lee (2006)
  21. C. Bishop, Novelty detection and neural network validation, in: Proceedings of the IEEE Conference on Vision, Image and Signal Processing, vol. 141, IET, 1994, pp. 217–222. (10.1049/ip-vis:19941330)
  22. 10.1016/S0167-8655(97)00049-4 / Pattern Recognit. Lett. / Outliers in statistical pattern recognition and an application to automatic chromosome classification by Ritter (1997)
  23. I. Merriam-Webster, Merriam-webster – an encyclopedia britannica company, May 2012. URL 〈http://www.merriam-webster.com/dictionary/novel/〉.
  24. 10.1145/1541880.1541882 / ACM Comput. Surv. (CSUR) / Anomaly detection by Chandola (2009)
  25. {'year': '1994', 'series-title': 'Outliers in Statistical Data, Wiley Series in Probability and Mathematical Statistics', 'author': 'Barnett', 'key': '10.1016/j.sigpro.2013.12.026_bib25'} / Outliers in Statistical Data, Wiley Series in Probability and Mathematical Statistics by Barnett (1994)
  26. 10.1016/j.sigpro.2003.07.018 / Signal Process. / Novelty detection: a review – part 1 by Markou (2003)
  27. 10.1016/j.sigpro.2003.07.019 / Signal Process. / Novelty detection by Markou (2003)
  28. {'key': '10.1016/j.sigpro.2013.12.026_bib28', 'first-page': '157', 'article-title': 'Novelty detection in learning systems', 'volume': '3', 'author': 'Marsland', 'year': '2003', 'journal-title': 'Neural Comput. Surv.'} / Neural Comput. Surv. / Novelty detection in learning systems by Marsland (2003)
  29. 10.1023/B:AIRE.0000045502.10941.a9 / Artif. Intell. Rev. / A survey of outlier detection methodologies by Hodge (2004)
  30. 10.3233/IDA-2006-10604 / Intell. Data Anal. / A comprehensive survey of numeric and symbolic outlier mining techniques by Agyemang (2006)
  31. Z. Bakar, R. Mohemad, A. Ahmad, M. Deris, A comparative study for outlier detection techniques in data mining, in: Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems, IEEE, 2006, pp. 1–6. (10.1109/ICCIS.2006.252287)
  32. {'key': '10.1016/j.sigpro.2013.12.026_bib32', 'first-page': '188', 'article-title': 'A survey of recent trends in one class classification', 'volume': 'vol. 6206', 'author': 'Khan', 'year': '2010'} / A survey of recent trends in one class classification by Khan (2010)
  33. {'year': '2001', 'series-title': 'Pattern Classification', 'author': 'Duda', 'key': '10.1016/j.sigpro.2013.12.026_bib33'} / Pattern Classification by Duda (2001)
  34. {'volume': 'vol. 4', 'year': '2006', 'author': 'Bishop', 'key': '10.1016/j.sigpro.2013.12.026_bib34'} by Bishop (2006)
  35. 10.1007/s11063-009-9106-4 / Neural Process. Lett. / Analysis of time series novelty detection strategies for synthetic and real data by Modenesi (2009)
  36. V. Chandola, A. Banerjee, V. Kumar, Outlier Detection: A Survey, Technical Report 07-017, University of Minnesota, 2007.
  37. J. Kittler, W. Christmas, T. de Campos, D. Windridge, F. Yan, J. Illingworth, M. Osman, Domain anomaly detection in machine perception: a system architecture and taxonomy, IEEE Trans. Pattern Anal. Mach. Intell. 99 (2013) 1. (10.1109/TPAMI.2013.209)
  38. {'key': '10.1016/j.sigpro.2013.12.026_bib38', 'first-page': '61', 'article-title': 'Anomaly, novelty, one-class classification', 'volume': '3', 'author': 'Bartkowiak', 'year': '2011', 'journal-title': 'Int. J. Comput. Inf. Syst. Ind. Manage. Appl.'} / Int. J. Comput. Inf. Syst. Ind. Manage. Appl. / Anomaly, novelty, one-class classification by Bartkowiak (2011)
  39. Y. Gatsoulis, E. Kerr, J. Condell, N. Siddique, T. McGinnity, Novelty detection for cumulative learning, in: Proceedings of the Conference on Towards Autonomous Robotic Systems, 2010, pp. 62–67.
  40. E. Kerr, Y. Gatsoulis, N.H. Siddique, J.V. Condell, T.M. McGinnity, Brief overview of novelty detection methods for robotic cumulative learning, in: Proceedings of the 21st National Conference on Artificial Intelligence and Cognitive Science, 2010, pp. 171–180.
  41. D. Miljkovic, Review of novelty detection methods, in: Proceedings of the 33rd International Convention (MIPRO), IEEE, 2010, pp. 593–598.
  42. 10.1007/978-90-481-3662-9_86 / Novel Algoritm. Tech. Telecommun. Netw. / Data mining based network intrusion detection system by Helali (2010)
  43. 10.1080/00401706.1969.10490657 / Technometrics / Procedures for detecting outlying observations in samples by Grubbs (1969)
  44. C. Aggarwal, P. Yu, Outlier detection with uncertain data, in: Proceedings of the SIAM International Conference on Data Mining, 2008, pp. 483–493. (10.1137/1.9781611972788.44)
  45. 10.1373/clinchem.2005.058339 / Clin. Chem. / Detection of outliers in reference distributions by Solberg (2005)
  46. 10.1109/TIT.1970.1054406 / IEEE Trans. Inf. Theory / On optimum recognition error and reject tradeoff by Chow (1970)
  47. {'year': '2008', 'series-title': 'Frontmatter', 'author': 'Scott', 'key': '10.1016/j.sigpro.2013.12.026_bib47'} / Frontmatter by Scott (2008)
  48. D. Filev, F. Tseng, Real time novelty detection modeling for machine health prognostics, in: Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), IEEE, 2006, pp. 529–534. (10.1109/NAFIPS.2006.365465)
  49. D. Filev, F. Tseng, Novelty detection based machine health prognostics, in: International Symposium on Evolving Fuzzy Systems, 2006, pp. 193–199. (10.1109/ISEFS.2006.251161)
  50. A. Flexer, E. Pampalk, G. Widmer, Novelty detection based on spectral similarity of songs, in: Proceedings of 6th International Conference on Music Information Retrieval, 2005, pp. 260–263.
  51. J. Ilonen, P. Paalanen, J. Kamarainen, H. Kalviainen, Gaussian mixture pdf in one-class classification: computing and utilizing confidence values, in: Proceedings of the 18th International Conference on Pattern Recognition (ICPR), vol. 2, IEEE, 2006, pp. 577–580. (10.1109/ICPR.2006.595)
  52. J. Larsen, Distribution of the Density of a Gaussian Mixture, Technical Report, Informatics and Mathematical Modelling, DTU, 2003.
  53. 10.1016/j.patcog.2006.01.005 / Pattern Recognit. / Feature representation and discrimination based on Gaussian mixture model probability densities – practices and algorithms by Paalanen (2006)
  54. N. Pontoppidan, J. Larsen, Unsupervised condition change detection in large diesel engines, in: Proceedings of the IEEE 13th Workshop on Neural Networks for Signal Processing, NNSP'03, IEEE, 2003, pp. 565–574. (10.1109/NNSP.2003.1318056)
  55. 10.1109/TKDE.2007.1009 / IEEE Trans. Knowl. Data Eng. / Conditional anomaly detection by Song (2007)
  56. 10.1007/s00170-004-2174-8 / Int. J. Adv. Manuf. Technol. / Novelty detection for practical pattern recognition in condition monitoring of multivariate processes by Zorriassatine (2005)
  57. 10.1007/978-3-540-78297-1_13 / Adv. Comput. Intell. Ind. Syst. / Automated novelty detection in industrial systems by Clifton (2008)
  58. D. Clifton, S. Hugueny, L. Tarassenko, A comparison of approaches to multivariate extreme value theory for novelty detection, in: Proceedings of the IEEE/SP 15th Workshop on Statistical Signal Processing, IEEE, 2009, pp. 13–16. (10.1109/SSP.2009.5278652)
  59. 10.1007/s11265-010-0513-6 / J. Signal Process. Syst. / Novelty detection with multivariate extreme value statistics by Clifton (2011)
  60. D. Clifton, S. Hugueny, L. Tarassenko, Pinning the tail on the distribution: a multivariate extension to the generalised Pareto distribution, in: IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2011, pp. 1–6. (10.1109/MLSP.2011.6064572)
  61. 10.1109/JSTSP.2012.2234081 / IEEE J. Sel. Top. Signal Process. / An extreme function theory for novelty detection by Clifton (2013)
  62. A. Hazan, J. Lacaille, K. Madani, Extreme value statistics for vibration spectra outlier detection, in: Proceedings of the 9th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, 2012.
  63. S. Hugueny, D. Clifton, L. Tarassenko, Novelty detection with multivariate extreme value theory, part II: an analytical approach to unimodal estimation, in: Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, IEEE, 2009, pp. 1–6. (10.1109/MLSP.2009.5306228)
  64. S. Roberts, Novelty detection using extreme value statistics, in: Proceedings of the IEEE Conference on Vision, Image and Signal Processing 146 (3) (1999) 124–129.
  65. S. Roberts, Extreme value statistics for novelty detection in biomedical data processing, in: Proceedings of the IEEE Conference on Science, Measurement and Technology, vol. 147, IET, 2000, pp. 363–367. (10.1049/ip-smt:20000841)
  66. 10.1115/1.1849240 / J. Dyn. Syst. Meas. Control / Structural damage classification using extreme value statistics by Sohn (2005)
  67. S. Sundaram, D. Clifton, I. Strachan, L. Tarassenko, S. King, Aircraft engine health monitoring using density modelling and extreme value statistics, in: Proceedings of the 6th International Conference on Condition Monitoring and Machine Failure Prevention Technologies, 2009.
  68. R. Gwadera, M. Atallah, W. Szpankowski, Markov models for identification of significant episodes, in: Proceedings of 5th SIAM International Conference on Data Mining, 2005, pp. 404–414. (10.1137/1.9781611972757.36)
  69. 10.1007/s10115-004-0174-5 / Knowl. Inf. Syst. / Reliable detection of episodes in event sequences by Gwadera (2005)
  70. A. Ihler, J. Hutchins, P. Smyth, Adaptive event detection with time-varying poisson processes, in: Proceedings of the 12th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), ACM, 2006, pp. 207–216. (10.1145/1150402.1150428)
  71. D. Janakiram, V. Adi Mallikarjuna Reddy, A. Phani Kumar, Outlier detection in wireless sensor networks using Bayesian belief networks, in: Proceedings of the 1st International Conference on Communication System Software and Middleware (Comsware), IEEE, 2006, pp. 1–6. (10.1109/COMSWA.2006.1665221)
  72. H.-J. Lee, S. Roberts, On-line novelty detection using the Kalman filter and extreme value theory, in: Proceedings of the 19th International Conference on Pattern Recognition (ICPR), 2008, pp. 1–4. (10.1109/ICPR.2008.4761918)
  73. 10.1007/BF02345078 / Med. Biol. Eng. Comput. / Linear and non-linear methods for automatic seizure detection in scalp electro-encephalogram recordings by McSharry (2002)
  74. P. McSharry, Detection of dynamical transitions in biomedical signals using nonlinear methods, in: Knowledge-Based Intelligent Information and Engineering Systems, Springer, 2004, pp. 483–490. (10.1007/978-3-540-30134-9_65)
  75. 10.1109/TMM.2011.2122247 / IEEE Trans. Multimed. / Probabilistic novelty detection for acoustic surveillance under real-world conditions by Ntalampiras (2011)
  76. 10.1007/978-3-642-24769-9_24 / Prog. Artif. Intell. / Novelty detection using graphical models for semantic room classification by Pinto (2011)
  77. 10.1049/el:20020467 / Electron. Lett. / Anomaly intrusion detection method based on HMM by Qiao (2002)
  78. 10.1109/TPAMI.2008.191 / IEEE Trans. Pattern Anal. Mach. Intell. / Factorial switching linear dynamical systems applied to physiological condition monitoring by Quinn (2009)
  79. C. Siaterlis, B. Maglaris, Towards multisensor data fusion for dos detection, in: Proceedings of the ACM Symposium on Applied Computing, SAC ’04, ACM, New York, NY, USA, 2004, pp. 439–446. (10.1145/967900.967992)
  80. {'key': '10.1016/j.sigpro.2013.12.026_bib80', 'first-page': '1513', 'article-title': 'Factorial switching Kalman filters for condition monitoring in neonatal intensive care', 'author': 'Williams', 'year': '2006', 'journal-title': 'Neural Inf. Process.'} / Neural Inf. Process. / Factorial switching Kalman filters for condition monitoring in neonatal intensive care by Williams (2006)
  81. W. Wong, A. Moore, G. Cooper, M. Wagner, Rule-based anomaly pattern detection for detecting disease outbreaks, in: Proceedings of the National Conference on Artificial Intelligence, Menlo Park, CA; Cambridge, MA; London, AAAI Press; MIT Press; 1999, 2002, pp. 217–223.
  82. W. Wong, A. Moore, G. Cooper, M. Wagner, Bayesian network anomaly pattern detection for disease outbreaks, in: Proceedings of the 20th International Conference on Machine Learning, vol. 20, AAAI Press, 2003, pp. 808–815.
  83. 10.1016/S0031-3203(02)00026-2 / Pattern Recognit. / Host-based intrusion detection using dynamic and static behavioral models by Yeung (2003)
  84. X. Zhang, P. Fan, Z. Zhu, A new anomaly detection method based on hierarchical HMM, in: Proceedings of the 4th International Conference on Parallel and Distributed Computing, Applications and Technologies, IEEE, 2003, pp. 249–252.
  85. 10.1016/j.ijar.2003.08.006 / Int. J. Approx. Reason. / An approach for fuzzy rule-base adaptation using on-line clustering by Angelov (2004)
  86. {'key': '10.1016/j.sigpro.2013.12.026_bib86', 'first-page': '129', 'article-title': 'Non-local manifold tangent learning', 'volume': '17', 'author': 'Bengio', 'year': '2005', 'journal-title': 'Adv. Neural Inf. Process. Syst.'} / Adv. Neural Inf. Process. Syst. / Non-local manifold tangent learning by Bengio (2005)
  87. {'key': '10.1016/j.sigpro.2013.12.026_bib87', 'first-page': '115', 'article-title': 'Non-local manifold parzen windows', 'volume': '18', 'author': 'Bengio', 'year': '2006', 'journal-title': 'Adv. Neural Inf. Process. Syst.'} / Adv. Neural Inf. Process. Syst. / Non-local manifold parzen windows by Bengio (2006)
  88. D. Erdogmus, R. Jenssen, Y. Rao, J. Principe, Multivariate density estimation with optimal marginal parzen density estimation and gaussianization, in: Proceedings of the 14th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, IEEE, 2004, pp. 73–82. (10.1109/MLSP.2004.1422961)
  89. 10.1007/s11263-009-0268-3 / Int. J. Comput. Vis. / Gaussian processes for object categorization by Kapoor (2010)
  90. M. Kemmler, E. Rodner, J. Denzler, One-class classification with Gaussian processes, in: Asian Conference on Computer Vision (ACCV), vol. 6493, 2011, pp. 489–500.
  91. {'key': '10.1016/j.sigpro.2013.12.026_bib91', 'first-page': '1238', 'article-title': 'Pseudo-density estimation for clustering with Gaussian processes', 'volume': '3971', 'author': 'Kim', 'year': '2006', 'journal-title': 'Adv. Neural Netw. (ISNN)'} / Adv. Neural Netw. (ISNN) / Pseudo-density estimation for clustering with Gaussian processes by Kim (2006)
  92. R. Ramezani, P. Angelov, X. Zhou, A fast approach to novelty detection in video streams using recursive density estimation, in: Proceedings of the 4th International IEEE Conference Intelligent Systems, IS'08, IEEE, vol. 2, 2008, pp. 14–22. (10.1109/IS.2008.4670523)
  93. S. Subramaniam, T. Palpanas, D. Papadopoulos, V. Kalogeraki, D. Gunopulos, Online outlier detection in sensor data using non-parametric models, in: Proceedings of the 32nd International Conference on Very Large Databases, VLDB Endowment, 2006, pp. 187–198.
  94. L. Tarassenko, A. Hann, A. Patterson, E. Braithwaite, K. Davidson, V. Barber, D. Young, Biosign™: multi-parameter monitoring for early warning of patient deterioration, in: Proceedings of the 3rd IEE International Seminar on Medical Applications of Signal Processing, IET, 2005, pp. 71–76. (10.1049/ic:20050334)
  95. 10.1093/bja/ael113 / Br. J. Anaesth. / Integrated monitoring and analysis for early warning of patient deterioration by Tarassenko (2006)
  96. {'key': '10.1016/j.sigpro.2013.12.026_bib96', 'first-page': '825', 'article-title': 'Manifold parzen windows', 'volume': '15', 'author': 'Vincent', 'year': '2002', 'journal-title': 'Adv. Neural Inf. Process. Syst.'} / Adv. Neural Inf. Process. Syst. / Manifold parzen windows by Vincent (2002)
  97. D. Yeung, C. Chow, Parzen-window network intrusion detectors, in: Proceedings of the 16th International Conference on Pattern Recognition, vol. 4, IEEE, 2002, pp. 385–388. (10.1109/ICPR.2002.1047476)
  98. D. Dasgupta, N. Majumdar, Anomaly detection in multidimensional data using negative selection algorithm, in: Proceedings of the Congress on Evolutionary Computation (CEC), vol. 2, IEEE, 2002, pp. 1039–1044. (10.1109/CEC.2002.1004386)
  99. 10.1109/TSMCB.2003.817026 / IEEE Trans. Syst. Man Cybern. Part B / A formal framework for positive and negative detection schemes by Esponda (2004)
  100. J. Gómez, F. González, D. Dasgupta, An immuno-fuzzy approach to anomaly detection, in: 12th IEEE International Conference on Fuzzy Systems (FUZZ '03), vol. 2, 2003, pp. 1219–1224. (10.1109/FUZZ.2003.1206605)
  101. 10.1023/A:1026195112518 / Genet. Program. Evolvable Mach. / Anomaly detection using real-valued negative selection by González (2003)
  102. 10.1007/978-3-540-45192-1_4 / Artif. Immune Syst. / An investigation of the negative selection algorithm for fault detection in refrigeration systems by Taylor (2003)
  103. {'volume': 'vol. 1', 'year': '1988', 'author': 'McLachlan', 'key': '10.1016/j.sigpro.2013.12.026_bib103'} by McLachlan (1988)
  104. {'key': '10.1016/j.sigpro.2013.12.026_bib104', 'series-title': 'Proceedings of the 3rd IASTED Conference on Artificial Intelligence and Applications', 'first-page': '457', 'article-title': 'Unsupervised learning of gamma mixture models using minimum message length', 'author': 'Agusta', 'year': '2003'} / Proceedings of the 3rd IASTED Conference on Artificial Intelligence and Applications / Unsupervised learning of gamma mixture models using minimum message length by Agusta (2003)
  105. 10.1093/bioinformatics/bti1125 / Bioinformatics / A gamma mixture model better accounts for among site rate heterogeneity by Mayrose (2005)
  106. 10.1016/j.csda.2006.09.032 / Comput. Stat. Data Anal. / Modelling nonlinear count time series with local mixtures of poisson autoregressions by Carvalho (2007)
  107. 10.1016/j.neucom.2004.11.018 / Neurocomputing / Robust Bayesian mixture modelling by Svensén (2005)
  108. A. Stranjak, P. Dutta, M. Ebden, A. Rogers, P. Vytelingum, A multi-agent simulation system for prediction and scheduling of aero engine overhaul, in: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems: Industrial Track, International Foundation for Autonomous Agents and Multiagent Systems, 2008, pp. 81–88.
  109. 10.1162/neco.1996.8.2.260 / Neural Comput. / Statistical independence and novelty detection with information preserving nonlinear maps by Parra (1996)
  110. A. Nairac, T. Corbett-Clark, R. Ripley, N. Townsend, L. Tarassenko, Choosing an appropriate model for novelty detection, in: Proceedings of the 5th International Conference on Artificial Neural Networks, IET, 1997, pp. 117–122. (10.1049/cp:19970712)
  111. 10.1080/00031305.1996.10474359 / Am. Stat. / Computing and graphing highest density regions by Hyndman (1996)
  112. 10.1214/aos/1176343003 / Ann. Stat. / Statistical inference using extreme order statistics by Pickands (1975)
  113. {'volume': 'vol. 33', 'year': '1997', 'author': 'Embrechts', 'key': '10.1016/j.sigpro.2013.12.026_bib113'} by Embrechts (1997)
  114. R. Fisher, L. Tippett, Limiting forms of the frequency distribution of the largest or smallest member of a sample, in: Proceedings of the Cambridge Philosophical Society, vol. 24, Cambridge University Press, 1928, pp. 180–190. (10.1017/S0305004100015681)
  115. D. Clifton, L. Tarassenko, N. McGrogan, D. King, S. King, P. Anuzis, Bayesian extreme value statistics for novelty detection in gas-turbine engines, in: Proceedings of the IEEE Aerospace Conference, IEEE, 2008, pp. 1–11. (10.1109/AERO.2008.4526423)
  116. 10.1006/jsvi.2002.5168 / J. Sound Vib. / Experimental validation of a structural health monitoring methodology by Worden (2003)
  117. 10.1023/B:DAMI.0000023676.72185.7c / Data Min. Knowl. Discov. / On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms by Yamanishi (2004)
  118. K. Yamanishi, J. Takeuchi, G. Williams, P. Milne, On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms, in: Proceedings of the 6th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), ACM, 2000, pp. 320–324. (10.1145/347090.347160)
  119. D. Agarwal, An empirical Bayes approach to detect anomalies in dynamic multidimensional arrays, in: Proceedings of the 5th IEEE International Conference on Data Mining, IEEE, 2005, pp. 26–33. (10.1109/ICDM.2005.22)
  120. 10.1007/s10115-006-0036-4 / Knowl. Inf. Syst. / Detecting anomalies in cross-classified streams by Agarwal (2007)
  121. 10.1016/S0360-8352(02)00215-2 / Comput. Ind. Eng. / Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes by Zorriassatine (2003)
  122. 10.1162/089976602317319009 / Neural Comput. / Mean-field approaches to independent component analysis by Højen-Sørensen (2002)
  123. 10.1162/089976603762553004 / Neural Comput. / Efficient greedy learning of Gaussian mixture models by Verbeek (2003)
  124. J. Zhang, Z. Ghahramani, Y. Yang, A probabilistic model for online document clustering with application to novelty detection, in: NIPS, 2005.
  125. 10.1016/j.engappai.2008.05.003 / Eng. Appl. Artif. Intell. / Concepts for novelty detection and handling based on a case-based reasoning process scheme by Perner (2009)
  126. {'key': '10.1016/j.sigpro.2013.12.026_bib126', 'first-page': '505', 'article-title': 'One-class classification by combining density and class probability estimation', 'volume': 'vol. 5211', 'author': 'Hempstalk', 'year': '2008'} / One-class classification by combining density and class probability estimation by Hempstalk (2008)
  127. D. Chen, M. Meng, Health status detection for patients in physiological monitoring, in: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 4921–4924. (10.1109/IEMBS.2011.6091219)
  128. {'key': '10.1016/j.sigpro.2013.12.026_bib128', 'first-page': '1391', 'article-title': 'A least-squares approach to direct importance estimation', 'volume': '10', 'author': 'Kanamori', 'year': '2009', 'journal-title': 'J. Mach. Learn. Res.'} / J. Mach. Learn. Res. / A least-squares approach to direct importance estimation by Kanamori (2009)
  129. 10.1007/s10115-010-0283-2 / Knowl. Inf. Syst. / Statistical outlier detection using direct density ratio estimation by Hido (2011)
  130. {'key': '10.1016/j.sigpro.2013.12.026_bib130', 'first-page': '10', 'article-title': 'Density ratio estimation', 'author': 'Sugiyama', 'year': '2010', 'journal-title': 'RIMS Kokyuroku'} / RIMS Kokyuroku / Density ratio estimation by Sugiyama (2010)
  131. 10.1016/S1350-4533(02)00146-7 / Med. Eng. Phys. / On-line novelty detection for artefact identification in automatic anaesthesia record keeping by Hoare (2002)
  132. 10.1162/neco.1994.6.2.270 / Neural Comput. / A probabilistic resource allocating network for novelty detection by Roberts (1994)
  133. 10.1198/016214505000001131 / J. Am. Stat. Assoc. / Outlier detection in multivariate time series by projection pursuit by Galeano (2006)
  134. 10.2116/analsci.21.161 / Anal. Sci. / Simultaneous wavelength selection and outlier detection in multivariate regression of near-infrared spectra by Chen (2005)
  135. 10.1273/cbij.3.30 / Chem-Bio Informat. / Detecting outlying samples in microarray data: a critical assessment of the effect of outliers on sample classification by Kadota (2003)
  136. 10.1109/49.339929 / IEEE J. Sel. Areas Commun. / Markov monitoring with unknown states by Smyth (1994)
  137. 10.1162/089976600300015619 / Neural Comput. / Variational learning for switching state-space models by Ghahramani (2000)
  138. M. Atallah, W. Szpankowski, R. Gwadera, Detection of significant sets of episodes in event sequences, in: Proceedings of the 4th IEEE International Conference on Data Mining, ICDM’04, IEEE, 2004, pp. 3–10. (10.1109/ICDM.2004.10090)
  139. A. Sebyala, T. Olukemi, L. Sacks, Active platform security through intrusion detection using naive Bayesian network for anomaly detection, in: London Communications Symposium, Citeseer, 2002.
  140. C. Kruegel, G. Vigna, Anomaly detection of web-based attacks, in: Proceedings of the 10th ACM Conference on Computer and Communications Security, ACM, 2003, pp. 251–261. (10.1145/948143.948144)
  141. C. Kruegel, D. Mutz, W. Robertson, F. Valeur, Bayesian event classification for intrusion detection, in: Proceedings of the 19th Annual Computer Security Applications Conference, IEEE, 2003, pp. 14–23. (10.1109/CSAC.2003.1254306)
  142. M. Mahoney, P. Chan, Learning nonstationary models of normal network traffic for detecting novel attacks, in: Proceedings of the 8th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), ACM, 2002, pp. 376–385. (10.1145/775047.775102)
  143. 10.1214/aoms/1177704472 / Ann. Math. Stat. / On estimation of a probability density function and mode by Parzen (1962)
  144. A. Frank, A. Asuncion, UCI machine learning repository, 2010.
  145. 10.1001/archinte.168.12.1300 / Arch. Internal Med. / Defining the incidence of cardiorespiratory instability in patients in step-down units using an electronic integrated monitoring system by Hravnak (2008)
  146. 10.1109/TSMCB.2003.817053 / IEEE Trans. Syst. Man Cybern. Part B / An approach to online identification of Takagi-Sugeno fuzzy models by Angelov (2004)
  147. R. Adams, I. Murray, D. MacKay, The Gaussian process density sampler, in: Advances in Neural Information Processing Systems (NIPS) 21, 2009, pp. 9–16. (10.1145/1553374.1553376)
  148. 10.1214/aoms/1177693055 / Ann. Math. Stat. / Procedures for reacting to a change in distribution by Lorden (1971)
  149. {'volume': 'vol. 104', 'year': '1993', 'author': 'Basseville', 'key': '10.1016/j.sigpro.2013.12.026_bib149'} by Basseville (1993)
  150. 10.1175/JAM2493.1 / J. Appl. Meteorol. Climatol. / A review and comparison of changepoint detection techniques for climate data by Reeves (2007)
  151. 10.1016/j.jnca.2005.07.004 / J. Netw. Comput. Appl. / Information sharing for distributed intrusion detection systems by Peng (2007)
  152. 10.1007/11760146_111 / Intell. Secur. Informat. / An anomaly detection algorithm for detecting attacks in wireless sensor networks by Van Phuong (2006)
  153. 10.1080/07474941003740997 / Seq. Anal. / State-of-the-art in Bayesian changepoint detection by Tartakovsky (2010)
  154. {'year': '2012', 'series-title': 'Parametric Statistical Change Point Analysis', 'author': 'Chen', 'key': '10.1016/j.sigpro.2013.12.026_bib154'} / Parametric Statistical Change Point Analysis by Chen (2012)
  155. S. Forrest, A. Perelson, L. Allen, R. Cherukuri, Self-nonself discrimination in a computer, in: Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, IEEE, 1994, pp. 202–212. (10.1109/RISP.1994.296580)
  156. F. Angiulli, C. Pizzuti, Fast outlier detection in high dimensional spaces, in: Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD '02, Springer-Verlag, London, UK, 2002, pp. 15–26. (10.1007/3-540-45681-3_2)
  157. S. Bay, M. Schwabacher, Mining distance-based outliers in near linear time with randomization and a simple pruning rule, in: Proceedings of the 9th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), ACM, 2003, pp. 29–38. (10.1145/956755.956758)
  158. S. Boriah, V. Chandola, V. Kumar, Similarity measures for categorical data: a comparative evaluation, in: Proceedings of the 8th SIAM International Conference on Data Mining, 2008, pp. 243–254. (10.1137/1.9781611972788.22)
  159. M. Breunig, H. Kriegel, R. Ng, J. Sander, LOF: identifying density-based local outliers, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, vol. 29, ACM, 2000, pp. 93–104. (10.1145/335191.335388)
  160. V. Chandola, S. Boriah, V. Kumar, Understanding Categorical Similarity Measures for Outlier Detection, Technical Report 08-008, University of Minnesota, 2008.
  161. 10.1007/s10115-005-0200-2 / Knowl. Inf. Syst. / SLOM by Chawla (2006)
  162. A. Ghoting, M. Otey, S. Parthasarathy, Loaded: link-based outlier and anomaly detection in evolving data sets, in: Proceedings of the 4th IEEE International Conference on Data Mining, ICDM’04, IEEE, 2004, pp. 387–390. (10.1109/ICDM.2004.10011)
  163. 10.1007/s10618-008-0093-2 / Data Min. Knowl. Discov. / Fast mining of distance-based outliers in high-dimensional datasets by Ghoting (2008)
  164. V. Hautamaki, I. Karkkainen, P. Franti, Outlier detection using k-nearest neighbour graph, in: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, IEEE, 2004, pp. 430–433. (10.1109/ICPR.2004.1334558)
  165. {'key': '10.1016/j.sigpro.2013.12.026_bib165', 'first-page': '79', 'article-title': 'Outlier detection using rough set theory', 'volume': 'vol. 3642', 'author': 'Jiang', 'year': '2005'} / Outlier detection using rough set theory by Jiang (2005)
  166. Y. Kou, C. Lu, D. Chen, Spatial weighted outlier detection, in: Proceedings of the SIAM Conference on Data Mining, 2006. (10.1137/1.9781611972764.71)
  167. 10.1007/s10618-005-0014-6 / Data Min. Knowl. Discov. / Fast distributed outlier detection in mixed-attribute data sets by Otey (2006)
  168. 10.1007/11604655_61 / Distrib. Comput. Internet Technol. / Distance-based outliers in sequences by Palshikar (2005)
  169. D. Pokrajac, A. Lazarevic, L. Latecki, Incremental local outlier detection for data streams, in: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2007, pp. 504–515. (10.1109/CIDM.2007.368917)
  170. M. Wu, C. Jermaine, Outlier detection by sampling with accuracy guarantees, in: Proceedings of the 12th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), ACM, 2006, pp. 767–772. (10.1145/1150402.1150501)
  171. 10.1007/s10115-006-0020-z / Knowl. Inf. Syst. / Detecting outlying subspaces for high-dimensional data by Zhang (2006)
  172. D. Barbará, Y. Li, J. Couto, COOLCAT: an entropy-based algorithm for categorical clustering, in: Proceedings of the 11th International Conference on Information and Knowledge Management, ACM, 2002, pp. 582–589. (10.1145/584792.584888)
  173. D. Barbará, Y. Li, J. Couto, J. Lin, S. Jajodia, Bootstrapping a data mining intrusion detection system, in: Proceedings of the ACM Symposium on Applied Computing, ACM, 2003, pp. 421–425. (10.1145/952532.952616)
  174. S. Budalakoti, A. Srivastava, R. Akella, E. Turkov, Anomaly Detection in Large Sets of High-Dimensional Symbol Sequences, Technical Report NASA TM-2006-214553, NASA Ames Research Center, 2006.
  175. {'key': '10.1016/j.sigpro.2013.12.026_bib175', 'first-page': '828', 'article-title': 'Learning shape for jet engine novelty detection', 'volume': '3973', 'author': 'Clifton', 'year': '2006', 'journal-title': 'Adv. Neural Netw. (ISNN)'} / Adv. Neural Netw. (ISNN) / Learning shape for jet engine novelty detection by Clifton (2006)
  176. 10.4028/www.scientific.net/KEM.347.305 / Key Eng. Mater. / A framework for novelty detection in jet engine vibration data by Clifton (2007)
  177. 10.1109/TFUZZ.2010.2043440 / IEEE Trans. Fuzzy Syst. / Applying the possibilistic c-means algorithm in kernel-induced spaces by Filippone (2010)
  178. 10.1016/S0167-8655(03)00003-5 / Pattern Recognit. Lett. / Discovering cluster-based local outliers by He (2003)
  179. 10.1016/j.eswa.2011.09.088 / Expert Syst. Appl. / Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing by Kim (2011)
  180. A. Srivastava, B. Zane-Ulman, Discovering recurring anomalies in text reports regarding complex space systems, in: Proceedings of the IEEE Aerospace Conference, IEEE, 2005, pp. 3853–3862. (10.1109/AERO.2005.1559692)
  181. A. Srivastava, Enabling the discovery of recurring anomalies in aerospace problem reports using high-dimensional clustering techniques, in: Proceedings of the IEEE Aerospace Conference, IEEE, 2006, pp. 1–17. (10.1109/AERO.2006.1656136)
  182. 10.1007/978-3-540-27772-9_60 / Adv. Web-Age Inf. Manage. / CD-trees by Sun (2004)
  183. Z. Syed, M. Saeed, I. Rubinfeld, Identifying high-risk patients without labeled training data: anomaly detection methodologies to predict adverse outcomes, in: AMIA Annual Symposium Proceedings, vol. 2010, American Medical Informatics Association, 2010, pp. 772–776.
  184. 10.1016/j.eswa.2008.02.037 / Exp. Syst. Appl. / Outlier identification and market segmentation using kernel-based clustering techniques by Wang (2009)
  185. J. Yang, W. Wang, CLUSEQ: efficient and effective sequence clustering, in: Proceedings of the 19th International Conference on Data Engineering, IEEE, 2003, pp. 101–112. (10.1109/ICDE.2003.1260785)
  186. 10.1016/j.patcog.2012.02.036 / Pattern Recognit. / Novelty detection in wildlife scenes through semantic context modelling by Yong (2012)
  187. 10.1007/s11042-011-0902-2 / Multimed. Tools Appl. / Wildlife video key-frame extraction based on novelty detection in semantic context by Yong (2013)
  188. 10.1007/s101150200013 / Knowl. Inf. Syst. / Findout by Yu (2002)
  189. 10.1007/978-3-540-73871-8_16 / Adv. Data Min. Appl. / Unsupervised outlier detection in sensor networks using aggregation tree by Zhang (2007)
  190. E. Knorr, R. Ng, Algorithms for mining distance-based outliers in large datasets, in: Proceedings of the International Conference on Very Large Data Bases, Citeseer, 1998, pp. 392–403.
  191. Y. Tao, X. Xiao, S. Zhou, Mining distance-based outliers from large databases in any metric space, in: Proceedings of the 12th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), ACM, 2006, pp. 394–403. (10.1145/1150402.1150447)
  192. {'key': '10.1016/j.sigpro.2013.12.026_bib192', 'first-page': '562', 'article-title': 'Hot: hypergraph-based outlier test for categorical data', 'volume': '2637', 'author': 'Wei', 'year': '2003', 'journal-title': 'Adv. Knowl. Discov. Data Min.'} / Adv. Knowl. Discov. Data Min. / Hot: hypergraph-based outlier test for categorical data by Wei (2003)
  193. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, 1994, pp. 487–499.
  194. S. Papadimitriou, H. Kitagawa, P. Gibbons, C. Faloutsos, LOCI: fast outlier detection using the local correlation integral, in: Proceedings of the 19th International Conference on Data Engineering, IEEE, 2003, pp. 315–326. (10.1109/ICDE.2003.1260802)
  195. A. Chiu, A. Fu, Enhancements on local outlier detection, in: Proceedings of the 7th International Database Engineering and Applications Symposium, IEEE, 2003, pp. 298–307. (10.1109/IDEAS.2003.1214939)
  196. 10.1007/3-540-47887-6_53 / Adv. Knowl. Discov. Data Min. / Enhancing effectiveness of outlier detections for low density patterns by Tang (2002)
  197. D. Ren, B. Wang, W. Perrizo, RDF: a density-based outlier detection method using vertical data representation, in: Proceedings of the 4th IEEE International Conference on Data Mining, ICDM’04, IEEE, 2004, pp. 503–506.
  198. 10.1007/s10115-005-0197-6 / Knowl. Inf. Syst. / Finding centric local outliers in categorical/numerical spaces by Yu (2006)
  199. 10.1007/s10115-005-0233-6 / Knowl. Inf. Syst. / Capabilities of outlier detection in large datasets, framework and methodologies by Tang (2007)
  200. P. Sun, S. Chawla, On local spatial outliers, in: Proceedings of the 4th IEEE International Conference on Data Mining, IEEE, 2004, pp. 209–216. (10.1109/ICDM.2004.10097)
  201. P. Sun, S. Chawla, B. Arunasalam, Mining for outliers in sequential databases, in: Proceedings of the 6th SIAM International Conference on Data Mining, vol. 124, Society for Industrial Mathematics, 2006. (10.1137/1.9781611972764.9)
  202. P. Chan, M. Mahoney, M. Arshad, A Machine Learning Approach to Anomaly Detection, Technical Report, Department of Computer Science, Florida Institute Technology Melbourne, 2003.
  203. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, Norwell, MA, USA, 1981. (10.1007/978-1-4757-0450-1)
  204. 10.1109/91.227387 / IEEE Trans. Fuzzy Syst. / A possibilistic approach to clustering by Krishnapuram (1993)
  205. A. Pires, C. Santos-Pereira, Using clustering and robust estimators to detect outliers in multivariate data, in: Proceedings of the International Conference on Robust Statistics, 2005.
  206. A. Vinueza, G. Grudic, Unsupervised Outlier Detection and Semi-Supervised Learning, Technical Report CU-CS-976-04, University of Colorado at Boulder, 2004.
  207. N. Wu, J. Zhang, Factor analysis based anomaly detection, in: Proceedings of the Information Assurance Workshop, IEEE Systems, Man and Cybernetics Society, IEEE, 2003, pp. 108–115. (10.1109/SMCSIA.2003.1232408)
  208. 10.1007/978-1-4613-0227-8_3 / Clust. Inf. Retr. / Finding topics in collections of documents by Ertöz (2003)
  209. {'issue': '3', 'key': '10.1016/j.sigpro.2013.12.026_bib209', 'first-page': '582', 'article-title': 'Support vector method for novelty detection', 'volume': '12', 'author': 'Schölkopf', 'year': '2000', 'journal-title': 'Adv. Neural Inf. Process. Syst.'} / Adv. Neural Inf. Process. Syst. / Support vector method for novelty detection by Schölkopf (2000)
  210. {'issue': '5', 'key': '10.1016/j.sigpro.2013.12.026_bib210', 'first-page': '1533', 'article-title': 'Unsupervised distributed novelty detection on scientific simulation data', 'volume': '7', 'author': 'Zhou', 'year': '2011', 'journal-title': 'J. Comput. Inf. Syst.'} / J. Comput. Inf. Syst. / Unsupervised distributed novelty detection on scientific simulation data by Zhou (2011)
  211. 10.3233/IDA-2009-0373 / Intell. Data Anal. / Novelty detection with application to data streams by Spinosa (2009)
  212. {'issue': '4', 'key': '10.1016/j.sigpro.2013.12.026_bib212', 'first-page': '66', 'article-title': 'A heuristic approach for sensor network outlier detection', 'volume': '1', 'author': 'Hassan', 'year': '2012', 'journal-title': 'Int. J. Res. Rev. Wirel. Sensor Netw. (IJRRWSN)'} / Int. J. Res. Rev. Wirel. Sensor Netw. (IJRRWSN) / A heuristic approach for sensor network outlier detection by Hassan (2012)
  213. T. Idé, S. Papadimitriou, M. Vlachos, Computing correlation anomaly scores using stochastic nearest neighbors, in: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM), IEEE, 2007, pp. 523–528. (10.1109/ICDM.2007.12)
  214. K. Onuma, H. Tong, C. Faloutsos, Tangent: a novel,‘surprise me’, recommendation algorithm, in: Proceedings of the 15th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), ACM, 2009, pp. 657–666. (10.1145/1557019.1557093)
  215. 10.1080/01431160110055804 / Int. J. Remote Sens. / Neural network classification and novelty detection by Augusteijn (2002)
  216. 10.1109/TKDE.2004.1269665 / IEEE Trans. Knowl. Data Eng. / An approach to novelty detection applied to the classification of image regions by Singh (2004)
  217. P. Crook, S. Marsland, G. Hayes, U. Nehmzow, A tale of two filters-on-line novelty detection, in: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA’02, vol. 4, IEEE, 2002, pp. 3894–3899. (10.1109/ROBOT.2002.1014330)
  218. I. Diaz, J. Hollmen, Residual generation and visualization for understanding novel process conditions, in: Proceedings of the International Joint Conference on Neural Networks, IJCNN’02, vol. 3, IEEE, 2002, pp. 2070–2075. (10.1109/IJCNN.2002.1007460)
  219. {'key': '10.1016/j.sigpro.2013.12.026_bib219', 'first-page': '113', 'article-title': 'Outlier detection using replicator neural networks', 'volume': '2454', 'author': 'Hawkins', 'year': '2002', 'journal-title': 'Data Wareh. Know. Discov.'} / Data Wareh. Know. Discov. / Outlier detection using replicator neural networks by Hawkins (2002)
  220. 10.1023/A:1007660820062 / Mach. Learn. / Supervised versus unsupervised binary-learning by feedforward neural networks by Japkowicz (2001)
  221. 10.1016/j.neucom.2006.05.013 / Neurocomputing / One-class document classification via neural networks by Manevitz (2007)
  222. B. Thompson, R. Marks, J. Choi, M. El-Sharkawi, M. Huang, C. Bunje, Implicit learning in autoencoder novelty assessment, in: Proceedings of the International Joint Conference on Neural Networks, IJCNN'02, vol. 3, IEEE, 2002, pp. 2878–2883. (10.1109/IJCNN.2002.1007605)
  223. G. Williams, R. Baxter, H. He, S. Hawkins, L. Gu, A comparative study of RNN for outlier detection in data mining, in: Proceedings of the IEEE International Conference on Data Mining, IEEE, 2002, pp. 709–712. (10.1109/ICDM.2002.1184035)
  224. S. Jakubek, T. Strasser, Fault-diagnosis using neural networks with ellipsoidal basis functions, in: Proceedings of the American Control Conference, vol. 5, IEEE, 2002, pp. 3846–3851. (10.1109/ACC.2002.1024528)
  225. 10.1016/S0167-8655(01)00133-7 / Pattern Recognit. Lett. / Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where unknown faults may occur by Li (2002)
  226. M.K. Albertini, R.F. de Mello, A self-organizing neural network for detecting novelties, in: Proceedings of the 2007 ACM Symposium on Applied Computing, SAC '07, ACM, New York, NY, USA, 2007, pp. 462–466. (10.1145/1244002.1244110)
  227. {'key': '10.1016/j.sigpro.2013.12.026_bib227', 'first-page': '28', 'article-title': 'Time series clustering for anomaly detection using competitive neural networks', 'volume': 'vol. 5629', 'author': 'Barreto', 'year': '2009'} / Time series clustering for anomaly detection using competitive neural networks by Barreto (2009)
  228. 10.1016/S0925-2312(02)00599-4 / Neurocomputing / On-line pattern analysis by evolving self-organizing maps by Deng (2003)
  229. 10.1016/j.neunet.2012.02.032 / Neural Netw. / Autonomous growing neural gas for applications with time constraint by García-Rodríguez (2012)
  230. 10.1021/ci700040r / J. Chem. Inf. Model. / Ligand-based virtual screening by novelty detection with self-organizing maps by Hristozov (2007)
  231. D. Kit, B. Sullivan, D. Ballard, Novelty detection using growing neural gas for visuo-spatial memory, in: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2011, pp. 1194–1200. (10.1109/IROS.2011.6094794)
  232. 10.1016/S0893-6080(02)00078-3 / Neural Netw. / A self-organising network that grows when required by Marsland (2002)
  233. 10.1016/j.robot.2004.10.006 / Robot. Auton. Syst. / On-line novelty detection for autonomous mobile robots by Marsland (2005)
  234. M. Ramadas, S. Ostermann, B. Tjaden, Detecting anomalous network traffic with self-organizing maps, in: Recent Advances in Intrusion Detection, Springer, 2003, pp. 36–54. (10.1007/978-3-540-45248-5_3)
  235. 10.1016/j.ymssp.2010.04.003 / Mech. Syst. Signal Process. / An online adaptive condition-based maintenance method for mechanical systems by Wu (2010)
  236. Y. Chen, B. Malin, Detection of anomalous insiders in collaborative environments via relational analysis of access logs, in: Proceedings of the 1st ACM Conference on Data and Application Security and Privacy, ACM, 2011, pp. 63–74. (10.1145/1943513.1943524)
  237. 10.1109/TDSC.2012.11 / IEEE Trans. Dependable Secur. Comput. / Detecting anomalous insiders in collaborative information systems by Chen (2012)
  238. {'key': '10.1016/j.sigpro.2013.12.026_bib238', 'first-page': '1893', 'article-title': 'Fast iterative kernel principal component analysis', 'volume': '8', 'author': 'Günter', 'year': '2007', 'journal-title': 'J. Mach. Learn. Res.'} / J. Mach. Learn. Res. / Fast iterative kernel principal component analysis by Günter (2007)
  239. 10.1016/j.patcog.2006.07.009 / Pattern Recognit. / Kernel PCA for novelty detection by Hoffmann (2007)
  240. 10.1145/1090191.1080118 / ACM SIGCOMM Comput. Commun. Rev. / Mining anomalies using traffic feature distributions by Lakhina (2005)
  241. 10.1007/s10994-008-5092-4 / Mach. Learn. / Incremental data-driven learning of a novelty detection model for one-class classification with application to high-dimensional noisy data by Kassab (2009)
  242. 10.1016/j.patrec.2011.01.019 / Pattern Recognit. Lett. / Feature extraction for novelty detection as applied to fault detection in machinery by McBain (2011)
  243. 10.1109/JSEN.2006.874015 / IEEE Sens. J. / On-line novelty detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions by Perera (2006)
  244. T. Ide, H. Kashima, Eigenspace-based anomaly detection in computer systems, in: Proceedings of the 11th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), ACM, 2004, pp. 440–449. (10.1145/1014052.1014102)
  245. M. Shyu, S. Chen, K. Sarinnapakorn, L. Chang, A Novel Anomaly Detection Scheme Based on Principal Component Classifier, Technical Report, DTIC Document, 2003.
  246. 10.1109/TSP.2003.814797 / IEEE Trans. Signal Process. / Anomaly detection in IP networks by Thottan (2003)
  247. 10.1007/978-3-642-13062-5_20 / Adv. Intell. Data Anal. IX / Novelty detection in projected spaces for structural health monitoring by Toivola (2010)
  248. 10.1016/j.patcog.2012.06.017 / Pattern Recognit. / L1 norm based KPCA for novelty detection by Xiao (2013)
  249. 10.1016/j.knosys.2007.11.007 / Knowl. Based Syst. / Evolving a dynamic predictive coding mechanism for novelty detection by Haggett (2008)
  250. 10.1038/nature03689 / Nature / Dynamic predictive coding by the retina by Hosoya (2005)
  251. 10.1109/5.58325 / Proc. IEEE / The self-organizing map by Kohonen (1990)
  252. {'key': '10.1016/j.sigpro.2013.12.026_bib252', 'first-page': '1', 'article-title': 'NSOM', 'author': 'Labib', 'year': '2002', 'journal-title': 'Netw. Secur.'} / Netw. Secur. / NSOM by Labib (2002)
  253. 10.1109/72.846732 / IEEE Trans. Neural Netw. / Dynamic self-organizing maps with controlled growth for knowledge discovery by Alahakoon (2000)
  254. J. Blackmore, R. Miikkulainen, Incremental grid growing: encoding high-dimensional structure into a two-dimensional feature map, in: Proceedings of the IEEE International Conference on Neural Networks, vol. 1, 1993, pp. 450–455. (10.1109/ICNN.1993.298599)
  255. 10.1016/0893-6080(94)90091-4 / Neural Netw. / Growing cell structures – a self-organizing network for unsupervised and supervised learning by Fritzke (1994)
  256. {'key': '10.1016/j.sigpro.2013.12.026_bib256', 'first-page': '625', 'article-title': 'A growing neural gas network learns topologies', 'volume': '7', 'author': 'Fritzke', 'year': '1995', 'journal-title': 'Adv. Neural Inf. Process. Syst.'} / Adv. Neural Inf. Process. Syst. / A growing neural gas network learns topologies by Fritzke (1995)
  257. {'volume': 'vol. 2', 'year': '2002', 'author': 'Jolliffe', 'key': '10.1016/j.sigpro.2013.12.026_bib257'} by Jolliffe (2002)
  258. R. Fujimaki, T. Yairi, K. Machida, An approach to spacecraft anomaly detection problem using kernel feature space, in: Proceedings of the 11th ACM International Conference on Knowledge Discovery in Data Mining (SIGKDD), ACM, 2005, pp. 401–410. (10.1145/1081870.1081917)
  259. 10.1162/089976698300017467 / Neural Comput. / Nonlinear component analysis as a kernel eigenvalue problem by Schölkopf (1998)
  260. 10.1109/TPAMI.2008.114 / IEEE Trans. Pattern Anal. Mach. Intell. / Principal component analysis based on l1-norm maximization by Kwak (2008)
  261. C. Noble, D. Cook, Graph-based anomaly detection, in: Proceedings of the 9th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), ACM, 2003, pp. 631–636. (10.1145/956804.956831)
  262. J. Sun, H. Qu, D. Chakrabarti, C. Faloutsos, Neighborhood formation and anomaly detection in bipartite graphs, in: Proceedings of the 5th IEEE International Conference on Data Mining, IEEE, 2005, pp. 418–425.
  263. J. Sun, Y. Xie, H. Zhang, C. Faloutsos, Less is more: compact matrix decomposition for large sparse graphs, in: Proceedings of the 7th SIAM International Conference in Data Mining, 2007. (10.1137/1.9781611972771.33)
  264. V. Chatzigiannakis, S. Papavassiliou, M. Grammatikou, B. Maglaris, Hierarchical anomaly detection in distributed large-scale sensor networks, in: Proceedings of the 11th IEEE Symposium on Computers and Communications, ISCC'06, IEEE, 2006, pp. 761–767. (10.1109/ISCC.2006.1691116)
  265. V. Lämsä, T. Raiko, Novelty detection by nonlinear factor analysis for structural health monitoring, in: Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP), IEEE, 2010, pp. 468–473. (10.1109/MLSP.2010.5588688)
  266. 10.1016/j.ymssp.2008.01.013 / Mech. Syst. Signal Process. / Fault detection using transient machine signals by Timusk (2008)
  267. 10.1016/S0167-8655(99)00087-2 / Pattern Recognit. Lett. / Support vector domain description by Tax (1999)
  268. 10.1109/TSMCC.2002.807277 / IEEE Trans. Syst. Man Cybern. Part C / Robust support vector machine with bullet hole image classification by Song (2002)
  269. W. Hu, Y. Liao, V. Vemuri, Robust anomaly detection using support vector machines, in: Proceedings of the International Conference on Machine Learning, 2003, pp. 282–289.
  270. G. Li, C. Wen, Z. Li, A new online learning with kernels method in novelty detection, in: Proceedings of the 37th Annual Conference on IEEE Industrial Electronics Society (IECON), IEEE, 2011, pp. 2311–2316. (10.1109/IECON.2011.6119670)
  271. {'key': '10.1016/j.sigpro.2013.12.026_bib271', 'first-page': '139', 'article-title': 'One-class SVMs for document classification', 'volume': '2', 'author': 'Manevitz', 'year': '2002', 'journal-title': 'J. Mach. Learn. Res.'} / J. Mach. Learn. Res. / One-class SVMs for document classification by Manevitz (2002)
  272. C. Campbell, K. Bennett, A linear programming approach to novelty detection, in: Proceedings of the Conference on Advances in Neural Information Processing Systems, vol. 13, The MIT Press, 2001, pp. 395–401.
  273. T. Le, D. Tran, W. Ma, D. Sharma, An optimal sphere and two large margins approach for novelty detection, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE, 2010, pp. 1–6. (10.1109/IJCNN.2010.5596456)
  274. 10.1007/978-3-642-20847-8_21 / Adv. Knowl. Discov. Data Min. / Multiple distribution data description learning algorithm for novelty detection by Le (2011)
  275. 10.1109/TNN.2010.2053853 / IEEE Trans. Neural Netw. / Fast support vector data descriptions for novelty detection by Liu (2010)
  276. 10.1016/j.eswa.2010.11.046 / Exp. Syst. Appl. / High-speed inline defect detection for TFT-LCD array process using a novel support vector data description by Liu (2011)
  277. 10.1007/s00521-011-0625-3 / Neural Comput. Appl. / Efficient support vector data descriptions for novelty detection by Peng (2012)
  278. 10.1109/TPAMI.2009.24 / IEEE Trans. Pattern Anal. Mach. Intell. / A small sphere and large margin approach for novelty detection using training data with outliers by Wu (2009)
  279. Y. Xiao, B. Liu, L. Cao, X. Wu, C. Zhang, Z. Hao, F. Yang, J. Cao, Multi-sphere support vector data description for outliers detection on multi-distribution data, in: Proceedings of the IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, 2009, pp. 82–87. (10.1109/ICDMW.2009.87)
  280. {'key': '10.1016/j.sigpro.2013.12.026_bib280', 'first-page': '836', 'article-title': 'Support vector machine in novelty detection for multi-channel combustion data', 'volume': '3973', 'author': 'Clifton', 'year': '2006', 'journal-title': 'Adv. Neural Netw. (ISNN)'} / Adv. Neural Netw. (ISNN) / Support vector machine in novelty detection for multi-channel combustion data by Clifton (2006)
  281. L. Clifton, H. Yin, D. Clifton, Y. Zhang, Combined support vector novelty detection for multi-channel combustion data, in: Proceedings of the IEEE International Conference on Networking, Sensing and Control, IEEE, 2007, pp. 495–500. (10.1109/ICNSC.2007.372828)
  282. P.F. Evangelista, M.J. Embrechts, B.K. Szymanski, Taming the curse of dimensionality in kernels and novelty detection, in: Applied Soft Computing Technologies: The Challenge of Complexity, Springer Verlag, 2006, pp. 431–444. (10.1007/3-540-31662-0_33)
  283. {'key': '10.1016/j.sigpro.2013.12.026_bib283', 'first-page': '1025', 'article-title': 'One-class novelty detection for seizure analysis from intracranial EEG', 'volume': '7', 'author': 'Gardner', 'year': '2006', 'journal-title': 'J. Mach. Learn. Res.'} / J. Mach. Learn. Res. / One-class novelty detection for seizure analysis from intracranial EEG by Gardner (2006)
  284. D.R. Hardoon, L.M. Manevitz, fMRI analysis via one-class machine learning techniques, in: Proceedings of the 19th International Joint Conference on Artificial intelligence, IJCAI’05, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2005, pp. 1604–1605.
  285. 10.1098/rsta.2006.1931 / Philos. Trans. R. Soc. A / Static and dynamic novelty detection methods for jet engine health monitoring by Hayton (2007)
  286. K. Heller, K. Svore, A. Keromytis, S. Stolfo, One class support vector machines for detecting anomalous windows registry accesses, in: Proceedings of the Workshop on Data Mining for Computer Security, 2003.
  287. A. Lazarevic, L. Ertoz, V. Kumar, A. Ozgur, J. Srivastava, A comparative study of anomaly detection schemes in network intrusion detection, in: Proceedings of the 3rd SIAM International Conference on Data Mining, vol. 3, SIAM, 2003, pp. 25–36. (10.1137/1.9781611972733.3)
  288. 10.1016/j.patrec.2006.02.019 / Pattern Recognit. Lett. / Application of LVQ to novelty detection using outlier training data by Lee (2006)
  289. J. Ma, S. Perkins, Online novelty detection on temporal sequences, in: Proceedings of the Ninth ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), ACM, 2003, pp. 613–618. (10.1145/956750.956828)
  290. J. Ma, S. Perkins, Time-series novelty detection using one-class support vector machines, in: Proceedings of the International Joint Conference on Neural Networks, vol. 3, IEEE, 2003, pp. 1741–1745. (10.1109/IJCNN.2003.1223670)
  291. A. Rabaoui, H. Kadri, N. Ellouze, New approaches based on one-class SVMs for impulsive sounds recognition tasks, in: Proceedings of the IEEE Workshop on Machine Learning for Signal Processing, IEEE, 2008, pp. 285–290. (10.1109/MLSP.2008.4685494)
  292. 10.4304/jcp.1.7.32-40 / J. Comput. / Parameter optimization of kernel-based one-class classifier on imbalance learning by Zhuang (2006)
  293. Z. Wu, W. Xie, J. Yu, Fuzzy c-means clustering algorithm based on kernel method, in: Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), IEEE, 2003, pp. 49–54.
  294. V. Roth, Outlier detection with one-class kernel fisher discriminants, in: Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS), 2004.
  295. 10.1162/neco.2006.18.4.942 / Neural Comput. / Kernel fisher discriminants for outlier detection by Roth (2006)
  296. 10.1109/TR.2010.2048740 / IEEE Trans. Reliab. / Anomaly detection through a Bayesian support vector machine by Sotiris (2010)
  297. 10.1109/TPAMI.2006.52 / IEEE Trans. Pattern Anal. Mach. Intell. / Estimation of high-density regions using one-class neighbor machines by Munoz (2006)
  298. Y. Li, A surface representation approach for novelty detection, in: Proceedings of the International Conference on Information and Automation (ICIA), IEEE, 2008, pp. 1464–1468. (10.1109/ICINFA.2008.4608233)
  299. 10.1007/11538059_42 / Adv. Intell. Comput. / An optimization model for outlier detection in categorical data by He (2005)
  300. 10.1007/11731139_67 / Adv. Knowl. Discov. Data Min. / A fast greedy algorithm for outlier mining by He (2006)
  301. S. Ando, Clustering needles in a haystack: an information theoretic analysis of minority and outlier detection, in: Proceedings of the 7th IEEE International Conference on Data Mining, ICDM’07, IEEE, 2007, pp. 13–22. (10.1109/ICDM.2007.53)
  302. E. Keogh, S. Lonardi, C. Ratanamahatana, Towards parameter-free data mining, in: Proceedings of the 10th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), ACM, 2004, pp. 206–215. (10.1145/1014052.1014077)
  303. 10.1007/s10115-006-0034-6 / Knowl. Inf. Syst. / Finding the most unusual time series subsequence by Keogh (2007)
  304. J. Lin, E. Keogh, A. Fu, H. Van Herle, Approximations to magic: finding unusual medical time series, in: Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems, IEEE, 2005, pp. 329–334. (10.1109/CBMS.2005.34)
  305. 10.1007/11811305_3 / Adv. Data Min. Appl. / Finding time series discords based on haar transform by Fu (2006)
  306. M. Gamon, Graph-based text representation for novelty detection, in: Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing, Association for Computational Linguistics, Stroudsburg, PA, USA, 2006, pp. 17–24. (10.3115/1654758.1654762)
  307. 10.1016/j.patcog.2009.07.002 / Pattern Recognit. / Information theoretic novelty detection by Filippone (2010)
  308. 10.1109/TSP.2010.2094609 / IEEE Trans. Signal Process. / A perturbative approach to novelty detection in autoregressive models by Filippone (2011)
  309. M. Filippone, G. Sanguinetti, Novelty Detection in Autoregressive Models Using Information Theoretic Measures, Technical Report CS-09-06, Department of Computer Science, University of Sheffield, 2009.
  310. 10.1016/j.visres.2008.09.007 / Vis. Res. / Bayesian surprise attracts human attention by Itti (2009)
Dates
Type When
Created 11 years, 8 months ago (Jan. 3, 2014, 5:19 a.m.)
Deposited 4 months ago (May 1, 2025, 6:47 a.m.)
Indexed 26 minutes ago (Sept. 6, 2025, 2:02 p.m.)
Issued 11 years, 3 months ago (June 1, 2014)
Published 11 years, 3 months ago (June 1, 2014)
Published Print 11 years, 3 months ago (June 1, 2014)
Funders 0

None

@article{Pimentel_2014, title={A review of novelty detection}, volume={99}, ISSN={0165-1684}, url={http://dx.doi.org/10.1016/j.sigpro.2013.12.026}, DOI={10.1016/j.sigpro.2013.12.026}, journal={Signal Processing}, publisher={Elsevier BV}, author={Pimentel, Marco A.F. and Clifton, David A. and Clifton, Lei and Tarassenko, Lionel}, year={2014}, month=jun, pages={215–249} }