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.