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  • Approximate LDA Technique for Dimensionality Reduction in the Small Sample Size Case

    Author(s)
    Paliwal, Kuldip
    Sharma, Alok
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Sharma, Alok
    Year published
    2011
    Metadata
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    Abstract
    The regularized linear discriminant analysis (LDA) technique overcomes the small sample size (SSS) problem by adding a regularization parameter to the eigenvalues of within-class scatter matrix. However, it has some drawbacks. In this paper we address its drawbacks and propose an improvement. The proposed technique is experimented on several datasets and promising results have been obtained.The regularized linear discriminant analysis (LDA) technique overcomes the small sample size (SSS) problem by adding a regularization parameter to the eigenvalues of within-class scatter matrix. However, it has some drawbacks. In this paper we address its drawbacks and propose an improvement. The proposed technique is experimented on several datasets and promising results have been obtained.
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    Journal Title
    Journal of Pattern Recognition Research
    Volume
    X
    Publisher URI
    http://www.jprr.org/index.php/jprr/article/view/370
    Subject
    Artificial Intelligence and Image Processing not elsewhere classified
    Artificial Intelligence and Image Processing
    Publication URI
    http://hdl.handle.net/10072/45639
    Collection
    • Journal articles

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