Computational Statistics

, Volume 33, Issue 4, pp 1939–1955 | Cite as

A correction of approximations used in sensitivity study of principal component analysis

  • Jacques Bénasséni
Short note


Principal component analysis is a method of dimensionality reduction based on the eigensystem of the covariance matrix of a set of multivariate observations. Analyzing the effects of some specific observations on this eigensystem is therefore of particular importance in the sensitivity study of the results. In this framework, approximations for the perturbed eigenvalues and eigenvectors when deleting one or several observations are useful from a computational standpoint. Indeed, they allow one to evaluate the effects of these observations without having to recompute the exact perturbed eigenvalues and eigenvectors. However, it turns out that some approximations which have been suggested are based on an incorrect application of matrix perturbation theory. The aim of this short note is to provide the correct formulations which are illustrated with a numerical study.


Covariance matrix Eigenvalues and eigenvectors Influential observations Perturbation theory 



The author is grateful to the reviewers for their careful reading of the paper and their helpful comments.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Univ Rennes, CNRS, IRMAR - UMR 6625RennesFrance

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