A Fast Kernel-Based Nonlinear Discriminant Analysis Method

The least squares solution of novel discriminant analysis method, based on kernel trick, is equivalent to kernel-based Fisher discriminant analysis. The discriminant vector of the novel method is efficiently solved from linear equations. Moreover, corresponding classifying strategy is very simple. The most striking advantage of the novel method is that only a few original training samples are sorted as “significant” nodes for constructing discriminant vector. As a result, corresponding testing is much more efficient than the nave kernel Fisher discriminant analysis. In addition an appropriative, optimized algorithm is developed to improve the efficiency of selecting “significant” nodes. Experiments on benchmarks and face databases show that the performance of the novel method is comparative to kernel-based Fisher discriminant analysis, with superiority in efficiency.