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  Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis

Kim, K., Franz, M., & Schölkopf, B.(2003). Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis (109). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DC4F-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-8C45-3
Genre: Report

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MPIK-TR-109.pdf (Publisher version), 784KB
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 Creators:
Kim, KI1, 2, Author              
Franz, M1, 2, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: A new method for performing a kernel principal component analysis is proposed. By kernelizing the generalized Hebbian algorithm, one can iteratively estimate the principal components in a reproducing kernel Hilbert space with only linear order memory complexity. The derivation of the method, a convergence proof, and preliminary applications in image hyperresolution are presented. In addition, we discuss the extension of the method to the online learning of kernel principal components.

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 Dates: 2003-06
 Publication Status: Published in print
 Pages: 13
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 109
BibTex Citekey: 2302
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
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Pages: - Volume / Issue: 109 Sequence Number: - Start / End Page: - Identifier: -