Abstract

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

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.