Kernel principal component analysis

  • Bernhard Schölkopf
  • Alexander Smola
  • Klaus-Robert Müller
Part IV: Signal Processing: Blind Source Separation Vector Quantization, and Self-Organization
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)

Abstract

A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.

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Copyright information

© Springer-Verlag 1997

Authors and Affiliations

  • Bernhard Schölkopf
    • 1
  • Alexander Smola
    • 2
  • Klaus-Robert Müller
    • 2
  1. 1.Max-Planck-Institut f. biol. KybernetikTübingenGermany
  2. 2.GMD FIRSTBerlinGermany

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