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Essence of kernel Fisher discriminant: KPCA plus LDA |
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| Authors: | Jian Yang [Author Vitae] Zhong Jin [Author Vitae] Jing-yu Yang |
| Affiliation: | a Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, PR China b Computer Vision Group, Aragon Institute of Engineering Research, University of Zaragoza, E-50018 Zaragoza, Spain c Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong d Centre of Computer Vision, University Autonoma of Barcelona, E-08193 Barcelona, Spain |
| Abstract: | In this paper, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i.e., KFD is equivalent to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). Based on this result, a more transparent KFD algorithm is proposed. That is, KPCA is first performed and then LDA is used for a second feature extraction in the KPCA-transformed space. Finally, the effectiveness of the proposed algorithm is verified using the CENPARMI handwritten numeral database. |
| Keywords: | Kernel-based methods Fisher linear discriminant analysis Principal component analysis Feature extraction Handwritten numeral recognition |
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