A Kernel Fractional-Step Nonlinear Discriminant Analysis for Pattern Recognition

Authors:
Guang Dai;Yuntao Qian;Sen Jia
Affiliations:
Zhejiang University, Hangzhou, P.R. China;Zhejiang University, Hangzhou, P.R. China;Zhejiang University, Hangzhou, P.R. China
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Year:
2004

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Abstract

Feature extraction is one of the most significant and fundamental problems in pattern recognition (PR). This paper introduces a novel kernel fractional-step nonlinear discriminant analysis (KF-NDA) for feature extraction in PR. It not only overcomes the limitation of failing for a nonlinear problem in the direct fractional-step linear discriminant analysis (DF-LDA), but also improves the generalization ability of traditional kernel nonlinear discriminant analysis (K-NDA). It is then applied to an experiment on face recognition, and the results demonstrate that this method is more effective than the existing methods.