Nonlinear component analysis as a kernel eigenvalue problem

Nonlinear component analysis as a kernel eigenvalue problem (1996)

by Bernhard Schölkopf , Alexander Smola , Klaus-Robert Müller

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Citations:	775 - 63 self

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Datum	Value	Source
TITLE	Nonlinear component analysis as a kernel eigenvalue problem	INFERENCE
AUTHOR NAME	Bernhard Schölkopf	user correction
AUTHOR NAME	Alexander Smola	user correction
AUTHOR ADDR	GMD First (Forschungszentrum Informationstechnik), Rudower Chaussee 5, 12489 Berlin	user correction
AUTHOR NAME	Klaus-Robert Müller	user correction
AUTHOR ADDR	GMD First (Forschungszentrum Informationstechnik), Rudower Chaussee 5, 12489 Berlin	user correction
ABSTRACT	We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we 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 5-pixel products in 16x16 images. We give the derivation of the method, along with a discussion of other techniques which can be made nonlinear with the kernel approach; and present first experimental results on nonlinear feature extraction for pattern recognition.	user correction
YEAR	1996	user correction
VENUE		user correction
VENUE TYPE		user correction
PAGES	1299--1319	INFERENCE
CITATIONS	3 found	ParsCit 1.0