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Nonlinear Component Analysis as a Kernel Eigenvalue Problem

Nonlinear Component Analysis as a Kernel Eigenvalue Problem 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computation MIT Press

Nonlinear Component Analysis as a Kernel Eigenvalue Problem

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References (25)

Publisher
MIT Press
Copyright
© 1998 Massachusetts Institute of Technology
ISSN
0899-7667
eISSN
1530-888X
DOI
10.1162/089976698300017467
Publisher site
See Article on Publisher Site

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 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.

Journal

Neural ComputationMIT Press

Published: Jul 1, 1998

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