DOI: 10.1002/wics.101. Principal component analysis

10.1002/wics.101

Crossref journal-article

Wiley

WIREs Computational Statistics (311)

Abstract

AbstractPrincipal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described by several inter‐correlated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. The quality of the PCA model can be evaluated using cross‐validation techniques such as the bootstrap and the jackknife. PCA can be generalized ascorrespondence analysis(CA) in order to handle qualitative variables and asmultiple factor analysis(MFA) in order to handle heterogeneous sets of variables. Mathematically, PCA depends upon the eigen‐decomposition of positive semi‐definite matrices and upon the singular value decomposition (SVD) of rectangular matrices. Copyright © 2010 John Wiley & Sons, Inc.This article is categorized under:Statistical and Graphical Methods of Data Analysis > Multivariate AnalysisStatistical and Graphical Methods of Data Analysis > Dimension Reduction

Bibliography

Abdi, H., & Williams, L. J. (2010). Principal component analysis. WIREs Computational Statistics, 2(4), 433â459. Portico.

Authors 2

Hervé Abdi (first)
Lynne J. Williams (additional)

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Dates

Type	When
Created	15 years, 1 month ago (July 6, 2010, 12:07 p.m.)
Deposited	6 months, 1 week ago (Feb. 22, 2025, 12:23 p.m.)
Indexed	39 minutes ago (Sept. 2, 2025, 11:06 a.m.)
Issued	15 years, 2 months ago (July 1, 2010)
Published	15 years, 2 months ago (July 1, 2010)
Published Online	15 years, 1 month ago (July 15, 2010)
Published Print	15 years, 2 months ago (July 1, 2010)

Funders 0

None

BibTeX

@article{Abdi_2010, title={Principal component analysis}, volume={2}, ISSN={1939-0068}, url={http://dx.doi.org/10.1002/wics.101}, DOI={10.1002/wics.101}, number={4}, journal={WIREs Computational Statistics}, publisher={Wiley}, author={Abdi, Hervé and Williams, Lynne J.}, year={2010}, month=jul, pages={433–459} }

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