Kernel principal component analysis

Bernhard Schölkopf, Alexander Smola, Klaus Robert Müller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1305 Citations (Scopus)


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 high-dimensional 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.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 1997 - 7th International Conference, Proceeedings
EditorsWulfram Gerstner, Alain Germond, Martin Hasler, Jean-Daniel Nicoud
PublisherSpringer Verlag
Number of pages6
ISBN (Print)3540636315, 9783540636311
Publication statusPublished - 1997
Event7th International Conference on Artificial Neural Networks, ICANN 1997 - Lausanne, Switzerland
Duration: 1997 Oct 81997 Oct 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th International Conference on Artificial Neural Networks, ICANN 1997

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1997.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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