Extending kernel fisher discriminant analysis with the weighted pairwise chernoff criterion

Authors:
Guang Dai;Dit-Yan Yeung;Hong Chang
Affiliations:
Department of Computer Science, Hong Kong University of Science and Technology, Kowloon, Hong Kong;Department of Computer Science, Hong Kong University of Science and Technology, Kowloon, Hong Kong;Department of Computer Science, Hong Kong University of Science and Technology, Kowloon, Hong Kong
Venue:
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Year:
2006

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Abstract

Many linear discriminant analysis (LDA) and kernel Fisher discriminant analysis (KFD) methods are based on the restrictive assumption that the data are homoscedastic. In this paper, we propose a new KFD method called heteroscedastic kernel weighted discriminant analysis (HKWDA) which has several appealing characteristics. First, like all kernel methods, it can handle nonlinearity efficiently in a disciplined manner. Second, by incorporating a weighting function that can capture heteroscedastic data distributions into the discriminant criterion, it can work under more realistic situations and hence can further enhance the classification accuracy in many real-world applications. Moreover, it can effectively deal with the small sample size problem. We have performed some face recognition experiments to compare HKWDA with several linear and nonlinear dimensionality reduction methods, showing that HKWDA consistently gives the best results.