Abstract
A generalized nonlinear discriminant analysis (GNDA) method is proposed, which implements Fisher discriminant analysis in a nonlinear mapping space. Linear discriminant analysis in the nonlinear mapping space corresponds to nonlinear discriminant analysis in an input space. GNDA suggests a unified framework of nonlinear discriminant analysis which includes the kernel Fisher discriminant analysis as a specific case. Experimental results on UCI data sets demonstrate the validity of our method.
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