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Neural Computation, Vol 10, 1299-1319, Copyright © 1998 by The MIT Press
LETTERS |
Bernhard Scholkopf, Alexander Smola and Klaus-Robert Muller
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 x 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.
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