Kernel Principal Component Analysis

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 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.

DOI: 10.1007/BFb0020217

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@inproceedings{Schlkopf1997KernelPC, title={Kernel Principal Component Analysis}, author={Bernhard Sch{\"{o}lkopf and Alexander J. Smola and Klaus-Robert M{\"{u}ller}, booktitle={ICANN}, year={1997} }