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 eeciently compute principal components in highh dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible dpixel 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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