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  • Fast principal component analysis using fixed-point algorithm

    Author(s)
    Sharma, Alok
    Pahwal, Kuldip K
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Year published
    2007
    Metadata
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    Abstract
    In this paper we present an efficient way of computing principal component analysis (PCA). The algorithm finds the desired number of leading eigenvectors with a computational cost that is much less than that from the eigenvalue decomposition (EVD) based PCA method. The mean squared error generated by the proposed method is very similar to the EVD based PCA method.In this paper we present an efficient way of computing principal component analysis (PCA). The algorithm finds the desired number of leading eigenvectors with a computational cost that is much less than that from the eigenvalue decomposition (EVD) based PCA method. The mean squared error generated by the proposed method is very similar to the EVD based PCA method.
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    Journal Title
    Pattern Recognition Letters
    Volume
    28
    Publisher URI
    http://www.elsevier.com/wps/find/journaldescription.cws_home/505619/description#description
    DOI
    https://doi.org/10.1016/j.patrec.2007.01.012
    Subject
    Cognitive and computational psychology
    Publication URI
    http://hdl.handle.net/10072/18520
    Collection
    • Journal articles

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