PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On the Convergence of Eigenspaces in Kernel Principal Component Analysis
Laurent Zwald and Gilles Blanchard
In: NIPS 2005, 5-8 Dec 2005, Vancouver.

Abstract

This paper presents a non-asymptotic statistical analysis of Kernel-PCA with a focus different from the one proposed in previous work on this topic. Here instead of considering the reconstruction error of KPCA we are interested in approximation error bounds for the eigenspaces themselves. We prove an upper bound depending on the spacing between eigenvalues but not on the dimensionality of the eigenspace. As a consequence this allows to infer stability results for these estimated spaces.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1342
Deposited By:Gilles Blanchard
Deposited On:28 November 2005