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Alexander Smola
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Publisher: Journals Gateway
Neural Computation (1998) 10 (5): 1299–1319.
Published: 01 July 1998
Abstract
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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 five-pixel products in 16 × 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.