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COLT
2004
Springer

Statistical Properties of Kernel Principal Component Analysis

8 years 9 months ago
Statistical Properties of Kernel Principal Component Analysis
The main goal of this paper is to prove inequalities on the reconstruction error for Kernel Principal Component Analysis. With respect to previous work on this topic, our contribution is twofold: (1) we give bounds that explicitly take into account the empirical centering step in this algorithm, and (2) we show that a “localized” approach allows to obtain more accurate bounds. In particular, we show faster rates of convergence towards the minimum reconstruction error; more precisely, we prove that the convergence rate can typically be faster than n−1/2. We also obtain a new relative bound on the error. A secondary goal, for which we present similar contributions, is to obtain convergence bounds for the partial sums of the biggest or smallest eigenvalues of the kernel Gram matrix towards eigenvalues of the corresponding kernel operator. These quantities are naturally linked to the KPCA procedure; furthermore these results can have applications to the study of various other kernel...
Laurent Zwald, Olivier Bousquet, Gilles Blanchard
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where COLT
Authors Laurent Zwald, Olivier Bousquet, Gilles Blanchard
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