A Hold-out method to correct PCA variance inflation

Pablo Garcia-Moreno, Antonio Artes-Rodriguez, Lars Kai Hansen

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    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
    Publication statusPublished - 2012
    Event3rd International Workshop on Cognitive Information Processing (CIP) - Baiona, Spain
    Duration: 28 May 201230 May 2012
    http://cip2012.tsc.uc3m.es/

    Workshop

    Workshop3rd International Workshop on Cognitive Information Processing (CIP)
    CountrySpain
    CityBaiona
    Period28/05/201230/05/2012
    Internet address

    Cite this

    Garcia-Moreno, P., Artes-Rodriguez, A., & Hansen, L. K. (2012). A Hold-out method to correct PCA variance inflation. In 2012 3rd International Workshop on Cognitive Information Processing (CIP) IEEE. https://doi.org/10.1109/CIP.2012.6232926