A Hold-out method to correct PCA variance inflation

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

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In this paper we analyze the problem of variance inflation experienced by the PCA algorithm when working in an ill-posed scenario where the dimensionality of the training set is larger than its sample size. In an earlier article a correction method based on a Leave-One-Out (LOO) procedure was introduced. We propose a Hold-out procedure whose computational cost is lower and, unlike the LOO method, the number of SVD's does not scale with the sample size. We analyze its properties from a theoretical and empirical point of view. Finally we apply it to a real classification scenario.
Original languageEnglish
Title of host publication2012 3rd International Workshop on Cognitive Information Processing (CIP)
Number of pages6
PublisherIEEE
Publication date2012
ISBN (print)978-1-4673-1877-8
DOIs
StatePublished

Workshop

Workshop3rd International Workshop on Cognitive Information Processing (CIP)
CountrySpain
CityBaiona
Period28/05/1230/05/12
Internet addresshttp://cip2012.tsc.uc3m.es/
CitationsWeb of Science® Times Cited: No match on DOI
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