Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in Kernel feature spaces

Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex Smola, Klaus Muller

Research output: Contribution to journalArticle

166 Citations (Scopus)

Abstract

We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Raylelgh coefficient, we propose nonlinear generalizations of Fisher's discriminant and oriented PCA using support vector kernel functions. Extensive simulations show the utility of our approach.

Original languageEnglish
Pages (from-to)623-628
Number of pages6
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume25
Issue number5
DOIs
Publication statusPublished - 2003 May 1
Externally publishedYes

Keywords

  • Fisher's discriminant
  • Kernel functions
  • Nonlinear feature extraction
  • Oriented PCA
  • Raylelgh coefficient
  • Support vector machine

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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