Crossref
journal-article
Elsevier BV
Signal Processing (78)
Authors
4
- Marco A.F. Pimentel (first)
- David A. Clifton (additional)
- Lei Clifton (additional)
- Lionel Tarassenko (additional)
References
310
Referenced
1,225
-
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
) 10.1007/978-3-540-72847-4_1
/ Pattern Recognit. Image Anal. / Known unknowns by Quinn (2007)- 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.
{'year': '2009', 'series-title': 'Novelty Detection', 'author': 'Tarassenko', 'key': '10.1016/j.sigpro.2013.12.026_bib4'}
/ Novelty Detection by Tarassenko (2009)10.1016/j.ymssp.2009.09.009
/ Mech. Syst. Signal Process. / Novelty detection in a changing environment by Surace (2010)10.1016/j.comnet.2007.02.001
/ Comput. Netw. / An overview of anomaly detection techniques by Patcha (2007){'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)-
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
) 10.1109/TPAMI.2006.196
/ IEEE Trans. Pattern Anal. Mach. Intell. / A neural network-based novelty detector for image sequence analysis by Markou (2006)10.1007/s10846-007-9146-9
/ J. Intell. Robotic Syst. / Real-time automated visual inspection using mobile robots by Vieira Neto (2007)10.1002/rob.20396
/ J. Field Robot. / Anytime online novelty and change detection for mobile robots by Sofman (2011)10.1109/SURV.2010.021510.00088
/ IEEE Commun. Surv. Tutor. / Outlier detection techniques for wireless sensor networks by Zhang (2010)-
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
) - H. Escalante, A comparison of outlier detection algorithms for machine learning, in: Proceedings of the International Conference on Communications in Computing, Citeseer, 2005.
-
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
) {'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)-
C. Sammut, G. Webb, Encyclopedia of Machine Learning. Springer, 2011. Springer reference.
(
10.1007/978-0-387-30164-8
) - 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.
10.1109/TKDE.2008.239
/ IEEE Trans. Knowl. Data Eng. / Learning from imbalanced data by He (2009){'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)-
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
) 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)- I. Merriam-Webster, Merriam-webster – an encyclopedia britannica company, May 2012. URL 〈http://www.merriam-webster.com/dictionary/novel/〉.
10.1145/1541880.1541882
/ ACM Comput. Surv. (CSUR) / Anomaly detection by Chandola (2009){'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)10.1016/j.sigpro.2003.07.018
/ Signal Process. / Novelty detection: a review – part 1 by Markou (2003)10.1016/j.sigpro.2003.07.019
/ Signal Process. / Novelty detection by Markou (2003){'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)10.1023/B:AIRE.0000045502.10941.a9
/ Artif. Intell. Rev. / A survey of outlier detection methodologies by Hodge (2004)10.3233/IDA-2006-10604
/ Intell. Data Anal. / A comprehensive survey of numeric and symbolic outlier mining techniques by Agyemang (2006)-
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
) {'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){'year': '2001', 'series-title': 'Pattern Classification', 'author': 'Duda', 'key': '10.1016/j.sigpro.2013.12.026_bib33'}
/ Pattern Classification by Duda (2001){'volume': 'vol. 4', 'year': '2006', 'author': 'Bishop', 'key': '10.1016/j.sigpro.2013.12.026_bib34'}
by Bishop (2006)10.1007/s11063-009-9106-4
/ Neural Process. Lett. / Analysis of time series novelty detection strategies for synthetic and real data by Modenesi (2009)- V. Chandola, A. Banerjee, V. Kumar, Outlier Detection: A Survey, Technical Report 07-017, University of Minnesota, 2007.
-
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
) {'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)- 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.
- 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.
- D. Miljkovic, Review of novelty detection methods, in: Proceedings of the 33rd International Convention (MIPRO), IEEE, 2010, pp. 593–598.
10.1007/978-90-481-3662-9_86
/ Novel Algoritm. Tech. Telecommun. Netw. / Data mining based network intrusion detection system by Helali (2010)10.1080/00401706.1969.10490657
/ Technometrics / Procedures for detecting outlying observations in samples by Grubbs (1969)-
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
) 10.1373/clinchem.2005.058339
/ Clin. Chem. / Detection of outliers in reference distributions by Solberg (2005)10.1109/TIT.1970.1054406
/ IEEE Trans. Inf. Theory / On optimum recognition error and reject tradeoff by Chow (1970){'year': '2008', 'series-title': 'Frontmatter', 'author': 'Scott', 'key': '10.1016/j.sigpro.2013.12.026_bib47'}
/ Frontmatter by Scott (2008)-
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
) -
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
) - 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.
-
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
) - J. Larsen, Distribution of the Density of a Gaussian Mixture, Technical Report, Informatics and Mathematical Modelling, DTU, 2003.
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)-
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
) 10.1109/TKDE.2007.1009
/ IEEE Trans. Knowl. Data Eng. / Conditional anomaly detection by Song (2007)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)10.1007/978-3-540-78297-1_13
/ Adv. Comput. Intell. Ind. Syst. / Automated novelty detection in industrial systems by Clifton (2008)-
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
) 10.1007/s11265-010-0513-6
/ J. Signal Process. Syst. / Novelty detection with multivariate extreme value statistics by Clifton (2011)-
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
) 10.1109/JSTSP.2012.2234081
/ IEEE J. Sel. Top. Signal Process. / An extreme function theory for novelty detection by Clifton (2013)- 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.
-
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
) - 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.
-
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
) 10.1115/1.1849240
/ J. Dyn. Syst. Meas. Control / Structural damage classification using extreme value statistics by Sohn (2005)- 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.
-
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
) 10.1007/s10115-004-0174-5
/ Knowl. Inf. Syst. / Reliable detection of episodes in event sequences by Gwadera (2005)-
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
) -
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
) -
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
) 10.1007/BF02345078
/ Med. Biol. Eng. Comput. / Linear and non-linear methods for automatic seizure detection in scalp electro-encephalogram recordings by McSharry (2002)-
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
) 10.1109/TMM.2011.2122247
/ IEEE Trans. Multimed. / Probabilistic novelty detection for acoustic surveillance under real-world conditions by Ntalampiras (2011)10.1007/978-3-642-24769-9_24
/ Prog. Artif. Intell. / Novelty detection using graphical models for semantic room classification by Pinto (2011)10.1049/el:20020467
/ Electron. Lett. / Anomaly intrusion detection method based on HMM by Qiao (2002)10.1109/TPAMI.2008.191
/ IEEE Trans. Pattern Anal. Mach. Intell. / Factorial switching linear dynamical systems applied to physiological condition monitoring by Quinn (2009)-
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
) {'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)- 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.
- 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.
10.1016/S0031-3203(02)00026-2
/ Pattern Recognit. / Host-based intrusion detection using dynamic and static behavioral models by Yeung (2003)- 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.
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){'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){'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)-
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
) 10.1007/s11263-009-0268-3
/ Int. J. Comput. Vis. / Gaussian processes for object categorization by Kapoor (2010)- 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.
{'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)-
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
) - 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.
-
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
) 10.1093/bja/ael113
/ Br. J. Anaesth. / Integrated monitoring and analysis for early warning of patient deterioration by Tarassenko (2006){'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)-
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
) -
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
) 10.1109/TSMCB.2003.817026
/ IEEE Trans. Syst. Man Cybern. Part B / A formal framework for positive and negative detection schemes by Esponda (2004)-
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
) 10.1023/A:1026195112518
/ Genet. Program. Evolvable Mach. / Anomaly detection using real-valued negative selection by González (2003)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){'volume': 'vol. 1', 'year': '1988', 'author': 'McLachlan', 'key': '10.1016/j.sigpro.2013.12.026_bib103'}
by McLachlan (1988){'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)10.1093/bioinformatics/bti1125
/ Bioinformatics / A gamma mixture model better accounts for among site rate heterogeneity by Mayrose (2005)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)10.1016/j.neucom.2004.11.018
/ Neurocomputing / Robust Bayesian mixture modelling by Svensén (2005)- 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.
10.1162/neco.1996.8.2.260
/ Neural Comput. / Statistical independence and novelty detection with information preserving nonlinear maps by Parra (1996)-
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
) 10.1080/00031305.1996.10474359
/ Am. Stat. / Computing and graphing highest density regions by Hyndman (1996)10.1214/aos/1176343003
/ Ann. Stat. / Statistical inference using extreme order statistics by Pickands (1975){'volume': 'vol. 33', 'year': '1997', 'author': 'Embrechts', 'key': '10.1016/j.sigpro.2013.12.026_bib113'}
by Embrechts (1997)-
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
) -
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
) 10.1006/jsvi.2002.5168
/ J. Sound Vib. / Experimental validation of a structural health monitoring methodology by Worden (2003)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)-
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
) -
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
) 10.1007/s10115-006-0036-4
/ Knowl. Inf. Syst. / Detecting anomalies in cross-classified streams by Agarwal (2007)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)10.1162/089976602317319009
/ Neural Comput. / Mean-field approaches to independent component analysis by Højen-Sørensen (2002)10.1162/089976603762553004
/ Neural Comput. / Efficient greedy learning of Gaussian mixture models by Verbeek (2003)- J. Zhang, Z. Ghahramani, Y. Yang, A probabilistic model for online document clustering with application to novelty detection, in: NIPS, 2005.
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){'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)-
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
) {'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)10.1007/s10115-010-0283-2
/ Knowl. Inf. Syst. / Statistical outlier detection using direct density ratio estimation by Hido (2011){'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)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)10.1162/neco.1994.6.2.270
/ Neural Comput. / A probabilistic resource allocating network for novelty detection by Roberts (1994)10.1198/016214505000001131
/ J. Am. Stat. Assoc. / Outlier detection in multivariate time series by projection pursuit by Galeano (2006)10.2116/analsci.21.161
/ Anal. Sci. / Simultaneous wavelength selection and outlier detection in multivariate regression of near-infrared spectra by Chen (2005)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)10.1109/49.339929
/ IEEE J. Sel. Areas Commun. / Markov monitoring with unknown states by Smyth (1994)10.1162/089976600300015619
/ Neural Comput. / Variational learning for switching state-space models by Ghahramani (2000)-
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
) - 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.
-
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
) -
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
) -
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
) 10.1214/aoms/1177704472
/ Ann. Math. Stat. / On estimation of a probability density function and mode by Parzen (1962)- A. Frank, A. Asuncion, UCI machine learning repository, 2010.
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)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)-
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
) 10.1214/aoms/1177693055
/ Ann. Math. Stat. / Procedures for reacting to a change in distribution by Lorden (1971){'volume': 'vol. 104', 'year': '1993', 'author': 'Basseville', 'key': '10.1016/j.sigpro.2013.12.026_bib149'}
by Basseville (1993)10.1175/JAM2493.1
/ J. Appl. Meteorol. Climatol. / A review and comparison of changepoint detection techniques for climate data by Reeves (2007)10.1016/j.jnca.2005.07.004
/ J. Netw. Comput. Appl. / Information sharing for distributed intrusion detection systems by Peng (2007)10.1007/11760146_111
/ Intell. Secur. Informat. / An anomaly detection algorithm for detecting attacks in wireless sensor networks by Van Phuong (2006)10.1080/07474941003740997
/ Seq. Anal. / State-of-the-art in Bayesian changepoint detection by Tartakovsky (2010){'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)-
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
) -
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
) -
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
) -
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
) -
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
) - V. Chandola, S. Boriah, V. Kumar, Understanding Categorical Similarity Measures for Outlier Detection, Technical Report 08-008, University of Minnesota, 2008.
10.1007/s10115-005-0200-2
/ Knowl. Inf. Syst. / SLOM by Chawla (2006)-
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
) 10.1007/s10618-008-0093-2
/ Data Min. Knowl. Discov. / Fast mining of distance-based outliers in high-dimensional datasets by Ghoting (2008)-
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
) {'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)-
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
) 10.1007/s10618-005-0014-6
/ Data Min. Knowl. Discov. / Fast distributed outlier detection in mixed-attribute data sets by Otey (2006)10.1007/11604655_61
/ Distrib. Comput. Internet Technol. / Distance-based outliers in sequences by Palshikar (2005)-
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
) -
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
) 10.1007/s10115-006-0020-z
/ Knowl. Inf. Syst. / Detecting outlying subspaces for high-dimensional data by Zhang (2006)-
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
) -
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
) - 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.
{'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)10.4028/www.scientific.net/KEM.347.305
/ Key Eng. Mater. / A framework for novelty detection in jet engine vibration data by Clifton (2007)10.1109/TFUZZ.2010.2043440
/ IEEE Trans. Fuzzy Syst. / Applying the possibilistic c-means algorithm in kernel-induced spaces by Filippone (2010)10.1016/S0167-8655(03)00003-5
/ Pattern Recognit. Lett. / Discovering cluster-based local outliers by He (2003)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)-
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
) -
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
) 10.1007/978-3-540-27772-9_60
/ Adv. Web-Age Inf. Manage. / CD-trees by Sun (2004)- 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.
10.1016/j.eswa.2008.02.037
/ Exp. Syst. Appl. / Outlier identification and market segmentation using kernel-based clustering techniques by Wang (2009)-
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
) 10.1016/j.patcog.2012.02.036
/ Pattern Recognit. / Novelty detection in wildlife scenes through semantic context modelling by Yong (2012)10.1007/s11042-011-0902-2
/ Multimed. Tools Appl. / Wildlife video key-frame extraction based on novelty detection in semantic context by Yong (2013)10.1007/s101150200013
/ Knowl. Inf. Syst. / Findout by Yu (2002)10.1007/978-3-540-73871-8_16
/ Adv. Data Min. Appl. / Unsupervised outlier detection in sensor networks using aggregation tree by Zhang (2007)- 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.
-
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
) {'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)- 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.
-
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
) -
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
) 10.1007/3-540-47887-6_53
/ Adv. Knowl. Discov. Data Min. / Enhancing effectiveness of outlier detections for low density patterns by Tang (2002)- 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.
10.1007/s10115-005-0197-6
/ Knowl. Inf. Syst. / Finding centric local outliers in categorical/numerical spaces by Yu (2006)10.1007/s10115-005-0233-6
/ Knowl. Inf. Syst. / Capabilities of outlier detection in large datasets, framework and methodologies by Tang (2007)-
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
) -
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
) - P. Chan, M. Mahoney, M. Arshad, A Machine Learning Approach to Anomaly Detection, Technical Report, Department of Computer Science, Florida Institute Technology Melbourne, 2003.
-
J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, Norwell, MA, USA, 1981.
(
10.1007/978-1-4757-0450-1
) 10.1109/91.227387
/ IEEE Trans. Fuzzy Syst. / A possibilistic approach to clustering by Krishnapuram (1993)- 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.
- A. Vinueza, G. Grudic, Unsupervised Outlier Detection and Semi-Supervised Learning, Technical Report CU-CS-976-04, University of Colorado at Boulder, 2004.
-
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
) 10.1007/978-1-4613-0227-8_3
/ Clust. Inf. Retr. / Finding topics in collections of documents by Ertöz (2003){'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){'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)10.3233/IDA-2009-0373
/ Intell. Data Anal. / Novelty detection with application to data streams by Spinosa (2009){'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)-
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
) -
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
) 10.1080/01431160110055804
/ Int. J. Remote Sens. / Neural network classification and novelty detection by Augusteijn (2002)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)-
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
) -
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
) {'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)10.1023/A:1007660820062
/ Mach. Learn. / Supervised versus unsupervised binary-learning by feedforward neural networks by Japkowicz (2001)10.1016/j.neucom.2006.05.013
/ Neurocomputing / One-class document classification via neural networks by Manevitz (2007)-
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
) -
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
) -
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
) 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)-
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
) {'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)10.1016/S0925-2312(02)00599-4
/ Neurocomputing / On-line pattern analysis by evolving self-organizing maps by Deng (2003)10.1016/j.neunet.2012.02.032
/ Neural Netw. / Autonomous growing neural gas for applications with time constraint by García-Rodríguez (2012)10.1021/ci700040r
/ J. Chem. Inf. Model. / Ligand-based virtual screening by novelty detection with self-organizing maps by Hristozov (2007)-
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
) 10.1016/S0893-6080(02)00078-3
/ Neural Netw. / A self-organising network that grows when required by Marsland (2002)10.1016/j.robot.2004.10.006
/ Robot. Auton. Syst. / On-line novelty detection for autonomous mobile robots by Marsland (2005)-
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
) 10.1016/j.ymssp.2010.04.003
/ Mech. Syst. Signal Process. / An online adaptive condition-based maintenance method for mechanical systems by Wu (2010)-
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
) 10.1109/TDSC.2012.11
/ IEEE Trans. Dependable Secur. Comput. / Detecting anomalous insiders in collaborative information systems by Chen (2012){'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)10.1016/j.patcog.2006.07.009
/ Pattern Recognit. / Kernel PCA for novelty detection by Hoffmann (2007)10.1145/1090191.1080118
/ ACM SIGCOMM Comput. Commun. Rev. / Mining anomalies using traffic feature distributions by Lakhina (2005)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)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)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)-
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
) - M. Shyu, S. Chen, K. Sarinnapakorn, L. Chang, A Novel Anomaly Detection Scheme Based on Principal Component Classifier, Technical Report, DTIC Document, 2003.
10.1109/TSP.2003.814797
/ IEEE Trans. Signal Process. / Anomaly detection in IP networks by Thottan (2003)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)10.1016/j.patcog.2012.06.017
/ Pattern Recognit. / L1 norm based KPCA for novelty detection by Xiao (2013)10.1016/j.knosys.2007.11.007
/ Knowl. Based Syst. / Evolving a dynamic predictive coding mechanism for novelty detection by Haggett (2008)10.1038/nature03689
/ Nature / Dynamic predictive coding by the retina by Hosoya (2005)10.1109/5.58325
/ Proc. IEEE / The self-organizing map by Kohonen (1990){'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)10.1109/72.846732
/ IEEE Trans. Neural Netw. / Dynamic self-organizing maps with controlled growth for knowledge discovery by Alahakoon (2000)-
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
) 10.1016/0893-6080(94)90091-4
/ Neural Netw. / Growing cell structures – a self-organizing network for unsupervised and supervised learning by Fritzke (1994){'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){'volume': 'vol. 2', 'year': '2002', 'author': 'Jolliffe', 'key': '10.1016/j.sigpro.2013.12.026_bib257'}
by Jolliffe (2002)-
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
) 10.1162/089976698300017467
/ Neural Comput. / Nonlinear component analysis as a kernel eigenvalue problem by Schölkopf (1998)10.1109/TPAMI.2008.114
/ IEEE Trans. Pattern Anal. Mach. Intell. / Principal component analysis based on l1-norm maximization by Kwak (2008)-
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
) - 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.
-
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
) -
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
) -
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
) 10.1016/j.ymssp.2008.01.013
/ Mech. Syst. Signal Process. / Fault detection using transient machine signals by Timusk (2008)10.1016/S0167-8655(99)00087-2
/ Pattern Recognit. Lett. / Support vector domain description by Tax (1999)10.1109/TSMCC.2002.807277
/ IEEE Trans. Syst. Man Cybern. Part C / Robust support vector machine with bullet hole image classification by Song (2002)- 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.
-
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
) {'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)- 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.
-
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
) 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)10.1109/TNN.2010.2053853
/ IEEE Trans. Neural Netw. / Fast support vector data descriptions for novelty detection by Liu (2010)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)10.1007/s00521-011-0625-3
/ Neural Comput. Appl. / Efficient support vector data descriptions for novelty detection by Peng (2012)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)-
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
) {'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)-
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
) -
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
) {'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)- 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.
10.1098/rsta.2006.1931
/ Philos. Trans. R. Soc. A / Static and dynamic novelty detection methods for jet engine health monitoring by Hayton (2007)- 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.
-
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
) 10.1016/j.patrec.2006.02.019
/ Pattern Recognit. Lett. / Application of LVQ to novelty detection using outlier training data by Lee (2006)-
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
) -
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
) -
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
) 10.4304/jcp.1.7.32-40
/ J. Comput. / Parameter optimization of kernel-based one-class classifier on imbalance learning by Zhuang (2006)- 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.
- V. Roth, Outlier detection with one-class kernel fisher discriminants, in: Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS), 2004.
10.1162/neco.2006.18.4.942
/ Neural Comput. / Kernel fisher discriminants for outlier detection by Roth (2006)10.1109/TR.2010.2048740
/ IEEE Trans. Reliab. / Anomaly detection through a Bayesian support vector machine by Sotiris (2010)10.1109/TPAMI.2006.52
/ IEEE Trans. Pattern Anal. Mach. Intell. / Estimation of high-density regions using one-class neighbor machines by Munoz (2006)-
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
) 10.1007/11538059_42
/ Adv. Intell. Comput. / An optimization model for outlier detection in categorical data by He (2005)10.1007/11731139_67
/ Adv. Knowl. Discov. Data Min. / A fast greedy algorithm for outlier mining by He (2006)-
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
) -
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
) 10.1007/s10115-006-0034-6
/ Knowl. Inf. Syst. / Finding the most unusual time series subsequence by Keogh (2007)-
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
) 10.1007/11811305_3
/ Adv. Data Min. Appl. / Finding time series discords based on haar transform by Fu (2006)-
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
) 10.1016/j.patcog.2009.07.002
/ Pattern Recognit. / Information theoretic novelty detection by Filippone (2010)10.1109/TSP.2010.2094609
/ IEEE Trans. Signal Process. / A perturbative approach to novelty detection in autoregressive models by Filippone (2011)- 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.
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) |
@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} }