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- W2054197572 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." @default.
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- W2054197572 date "2004-08-23" @default.
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- W2054197572 title "A kernel fractional-step nonlinear discriminant analysis for pattern recognition" @default.
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- W2054197572 doi "https://doi.org/10.1109/icpr.2004.47" @default.
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