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Kernel principal component analysis (1999)
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| Venue: | ADVANCES IN KERNEL METHODS - SUPPORT VECTOR LEARNING |
| Citations: | 274 - 7 self |
BibTeX
@INPROCEEDINGS{Scholkopf99kernelprincipal,
author = {Bernhard Scholkopf and Alexander Smola and Klaus-Robert Müller},
title = {Kernel principal component analysis},
booktitle = {ADVANCES IN KERNEL METHODS - SUPPORT VECTOR LEARNING},
year = {1999},
pages = {327--352},
publisher = {MIT Press}
}
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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 d-pixel products in images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.
Keyphrases
kernel principal component analysis high-dimensional feature space pattern recognition new method nonlinear form integral operator kernel function nonlinear map possible d-pixel product principal component present experimental result principal component analysis polynomial feature extraction
