Kernel Projection Machine: a New Tool for Pattern Recognition

Gilles Blanchard (1) , Pascal Massart (2) , Régis Vert (3) , Laurent Zwald (2)

1 Fraunhofer FIRST.IDA - Department Intelligent Data Analysis [Berlin]
2 LM-Orsay - Laboratoire de Mathématiques d'Orsay
3 LRI - Laboratoire de Recherche en Informatique

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.

Domaines: Machine Learning [stat.ML]

Gilles Blanchard, Pascal Massart, Régis Vert, Laurent Zwald. Kernel Projection Machine: a New Tool for Pattern Recognition. nips, 2004, -, Canada. ⟨hal-00373801⟩