Abstract
Kernel Discriminant Anlaysis(KDA) and Kernel Principal Component Analysis(KPCA) are the nonlinear extensions of Linear Discriminant Analysis(LDA) and Principal Component Analysis(PCA) respectively.In this paper,we presented a feature extraction algorithm by combing KDA and KPCA to extract reliable and robust features for recognition.Furthermore,a generalized nearest feature line(GNFL) method was also presented for constructing powerful classifier.The performance of the proposed method was demonstrated through real data.
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