Abstract
In this paper, a reformative kernel Fisher discriminant (KFD) algorithm with fuzzy set theory is studied. The KFD algorithm is effective to extract nonlinear discriminative features of input samples using the kernel trick. However, the conventional KFD algorithm assumes the same level of relevance of each sample to the corresponding class. In this paper, a fuzzy kernel Fisher discriminant (FKFD) algorithm is proposed. Distribution information of samples is represented with fuzzy membership degree and this information is utilized to redefine corresponding scatter matrices which are different to the conventional KFD algorithm and effective to extract discriminative features from overlapping (outlier) samples. Experimental results on the ORL face database demonstrate the effectiveness of the proposed method.
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