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2004

Kernel Projection Machine: a New Tool for Pattern Recognition

10 years 1 days ago
Kernel Projection Machine: a New Tool for Pattern Recognition
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.
Laurent Zwald, Régis Vert, Gilles Blanchard
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where NIPS
Authors Laurent Zwald, Régis Vert, Gilles Blanchard, Pascal Massart
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