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    Boosting performance for 2D linear discriminant analysis via regression

    Access Status
    Fulltext not available
    Authors
    Nguyen, Nam
    Liu, Wan-Quan
    Venkatesh, Svetha
    Date
    2008
    Type
    Conference Paper
    
    Metadata
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    Citation
    Nguyen, N. and Liu, W. and Venkatesh, S. 2008. Boosting performance for 2D linear discriminant analysis via regression, in 19th International Conference on Pattern Recognition (ICPR), Dec 8-11 2008. Tampa, Florida: IEEE.
    Source Title
    the 19th International Conference on Pattern Recognition
    Source Conference
    ICPR 2008
    DOI
    10.1109/ICPR.2008.4761898
    ISBN
    9781424421756
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/3201
    Collection
    • Curtin Research Publications
    Abstract

    Two dimensional linear discriminant analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an extension of 2DLDA with the introduction of a regularization parameter to deal with the small sample size problem. RR-2DLDA integrates ridge regression into Regularized 2DLDA to balance the distances among different classes after the transformation. These proposed algorithms overcome the limitations of 2DLDA and boost recognition accuracy. The experimental results on the Yale, PIE and FERET databases showed that RR-2DLDA is superior not only to 2DLDA but also other state-of-the-art algorithms.

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