Essence of kernel Fisher discriminant: KPCA plus LDA

Jian Yang, Zhong Jin, Jing Yu Yang, David Zhang, Alejandro F. Frangi

    Research output: Contribution to journalArticleResearchpeer-review

    167 Citations (Scopus)


    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. © 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
    Original languageEnglish
    Pages (from-to)2097-2100
    JournalPattern Recognition
    Issue number10
    Publication statusPublished - 1 Oct 2004


    • Feature extraction
    • Fisher linear discriminant analysis
    • Handwritten numeral recognition
    • Kernel-based methods
    • Principal component analysis


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