NDLI: An Approximate Version of Kernel PCA

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An Approximate Version of Kernel PCA

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An Approximate Version of Kernel PCA

Content Provider	IEEE Xplore Digital Library
Author	Martin, S.
Copyright Year	2006
Description	Author affiliation: Sandia Nat. Labs., Albuquerque, NM (Martin, S.)
Abstract	We propose an analog of kernel principal component analysis (kernel PCA). Our algorithm is based on an approximation of PCA which uses Gram-Schmidt orthonormalization. We combine this approximation with support vector machine kernels to obtain a nonlinear generalization of PCA. By using our approximation to PCA we are able to provide a more easily computed (in the case of many data points) and readily interpretable version of kernel PCA. After demonstrating our algorithm on some examples, we explore its use in applications to fluid flow and microarray data
Sponsorship	IEEE CPS
Starting Page	239
Ending Page	244
File Size	552596
Page Count	6
File Format	PDF
ISBN	0769527353
DOI	10.1109/ICMLA.2006.13
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Data analysis Laboratories Data preprocessing Fluid flow Independent component analysis Approximation algorithms Covariance matrix Kernel Principal component analysis
Content Type	Text
Resource Type	Article

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