Abstract
It has been proved a lot of linear feature extraction methods can be generalized to the nonlinear learning methods by using kernel methods. In this paper, a new nonlinear learning method of optimal transformation and cluster centers (OT-CC) is presented by using kernel technique. It is named as optimal transformation and cluster centers algorithm of kernel space (KOT-CC), which is a powerful technique for extracting nonlinear discriminant features and is very effective in solving pattern recognition problems where the overlap between patterns is serious. A large number of experiments demonstrate the new algorithm outperforms OT-CC and kernel fisher discriminant analysis (KFDA).
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