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

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

787 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)230-244
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number2
Publication statusPublished - 1 Feb 2005


  • 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

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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