Essence of kernel Fisher discriminant: KPCA plus LDA

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

175 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.
Original languageEnglish
Pages (from-to)2097-2100
Number of pages4
JournalPattern Recognition
Issue number10
Publication statusPublished - 1 Oct 2004


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

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


Dive into the research topics of 'Essence of kernel Fisher discriminant: KPCA plus LDA'. Together they form a unique fingerprint.

Cite this