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ICANN
1997
Springer

Kernel Principal Component Analysis

10 years 3 months ago
Kernel Principal Component Analysis
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can e ciently 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.
Bernhard Schölkopf, Alex J. Smola, Klaus-Robe
Added 08 Aug 2010
Updated 08 Aug 2010
Type Conference
Year 1997
Where ICANN
Authors Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller
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