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Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis

MPS-Authors
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Kim,  KI
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83919

Franz,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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MPIK-TR-109.pdf
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Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DC4F-3
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.