Kernel Projection Machine: A New Tool for Pattern Recognition
Abstract
This paper investigates the effect of Kernel Principal Component Analy- sis (KPCA) within the classification framework, essentially the regular- ization 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 reg- ularization point of view and we propose a new algorithm called Ker- nel 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.
Cite
Text
Zwald et al. "Kernel Projection Machine: A New Tool for Pattern Recognition." Neural Information Processing Systems, 2004.Markdown
[Zwald et al. "Kernel Projection Machine: A New Tool for Pattern Recognition." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/zwald2004neurips-kernel/)BibTeX
@inproceedings{zwald2004neurips-kernel,
title = {{Kernel Projection Machine: A New Tool for Pattern Recognition}},
author = {Zwald, Laurent and Blanchard, Gilles and Massart, Pascal and Vert, Régis},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1649-1656},
url = {https://mlanthology.org/neurips/2004/zwald2004neurips-kernel/}
}