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

Jacques Bénasséni ()
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Jacques Bénasséni: Univ Rennes, CNRS, IRMAR - UMR 6625

Computational Statistics, 2018, vol. 33, issue 4, No 16, 1939-1955

Abstract: Abstract 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.

Keywords: Covariance matrix; Eigenvalues and eigenvectors; Influential observations; Perturbation theory (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s00180-017-0790-7

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