Kernel principal component analysis

Bernhard Schölkopf, Alexander Smola, Klaus Robert Müller

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

    1452 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
    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

    Bibliographical note

    Publisher Copyright:
    © Springer-Verlag Berlin Heidelberg 1997.

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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