PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernel Projection Machine: a New Tool for Pattern Recognition
Gilles Blanchard, Pascal Massart, Regis Vert and Laurent Zwald
In: NIPS 2004, 13-16 Dec 2004, Vancouver, Canada.


This paper investigates the effect of Kernel Principal Component Analysis (KPCA) within the classification framework, essentially the regularization properties of this dimensionality reduction method. KPCA has been previously used as a pre-processing step before applying an SVM but we point out that this method is somewhat redundant from a regularization point of view and we propose a new algorithm called "Kernel Projection Machine" to avoid this redundancy, based on an analogy with the statistical framework of regression for a Gaussian white noise model. Preliminary experimental results show that this algorithm reaches the same performances as an SVM.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:509
Deposited By:Gilles Blanchard
Deposited On:24 December 2004