Abstract
Kernel Optimal Unsupervised Discriminant Projection (KOUDP) is presented in this paper. The proposed method first maps the input data into a potentially much higher dimensional feature space by virtue of nonlinear kernel trick, and in such a way, nonlinear features is extracted by running UDP on the kernel matrix. The singularity problem of the non-local scatter matrix due to small sample size problem occurred in UDP is avoided. Experimental results on YALE database indicate that the proposed KOUDP method achieves higher recognition rate than the UDP method and other kernel-based learning algorithms.KeywordsKernelUnsupervised discriminant projectionNonlinear featuresKernel matrix
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