KPCA plus LDA: A complete kernel fisher discriminant framework for feature extraction and recognition

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

    Research output: Contribution to journalArticleResearchpeer-review

    746 Citations (Scopus)

    Abstract

    This paper examines the theory of kernel Fisher discriminant analysis (KFD) in a Hilbert space and develops a two-phase KFD framework, i.e., kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). This framework provides novel insights into the nature of KFD. Based on this framework, the authors propose a complete kernel Fisher discriminant analysis (CKFD) algorithm. CKFD can be used to carry out discriminant analysis in "double discriminant subspaces." The fact that, it can make full use of two kinds of discriminant information, regular and irregular, makes CKFD a more powerful discriminator. The proposed algorithm was tested and evaluated using the FERET face database and the CENPARMI handwritten numeral database. The experimental results show that CKFD outperforms other KFD algorithms. © 2005 IEEE.
    Original languageEnglish
    Pages (from-to)230-244
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Volume27
    DOIs
    Publication statusPublished - 1 Jan 2005

    Keywords

    • Face recognition
    • Feature extraction
    • Fisher linear discriminant analysis (LDA or FLD)
    • Handwritten digit recognition
    • Kernel-based methods
    • Machine learning
    • Principal component analysis (PCA)
    • Subspace methods

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