Title: A kernel fractional-step nonlinear discriminant analysis for pattern recognition
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.
Publication Year: 2004
Publication Date: 2004-08-23
Language: en
Type: article
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Cited By Count: 11
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