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  Nonlinear Component Analysis as a Kernel Eigenvalue Problem

Schölkopf, B., Smola, A., & Müller, K.-R.(1996). Nonlinear Component Analysis as a Kernel Eigenvalue Problem (44). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-EAFE-C Version Permalink: http://hdl.handle.net/21.11116/0000-0002-8E6E-4
Genre: Report

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 Creators:
Schölkopf, B1, 2, Author              
Smola, AJ, Author              
Müller, K-R, Author              
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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 16 x 16 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.

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 Dates: 1996-12
 Publication Status: Published in print
 Pages: -
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 44
BibTex Citekey: 1509
 Degree: -

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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
Source Genre: Series
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Pages: - Volume / Issue: 44 Sequence Number: - Start / End Page: - Identifier: -