Robust Kernel Fisher Discriminant Analysis
نویسنده
چکیده
Kernel methods have become standard tools for solving classification and regression problems in statistics. An example of a kernel based classification method is Kernel Fisher discriminant analysis (KFDA), a kernel based extension of linear discriminant analysis (LDA), which was proposed by Mika et al. (1999). As in the case of LDA, the classification performance of KFDA deteriorates in the presence of outliers. Though many papers dealing with robust LDA have appeared in the literature, this is not the case for KFDA. In this paper we propose a procedure for robust KFDA. We investigate the performance of the proposed method in a simulation study and conclude that it has merit.
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