Kernel principal component analysis

Bernhard Schölkopf, Alexander Smola, Klaus Robert Müller

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

863 Citations (Scopus)

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 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
Pages583-588
Number of pages6
ISBN (Print)3540636315, 9783540636311
DOIs
Publication statusPublished - 1997
Externally publishedYes
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)
Volume1327
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Artificial Neural Networks, ICANN 1997
Country/TerritorySwitzerland
CityLausanne
Period97/10/897/10/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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