Kernel Choice for Unsupervised Kernel Methods

Abstract

In kernel methods, choosing a suitable kernel is indispensable for favorable results. While cross-validation is a useful method of the kernel and parameter choice for supervised learning such as the support vector machines, there are no well-founded methods, have been established in general for unsupervised learning. We focus on kernel principal component analysis (kernel PCA) and kernel canonical correlation analysis (kernel CCA), which are the nonlinear extension of principal component analysis (PCA) and canonical correlation analysis (CCA), respectively. Both of these methods have been used effectively for extracting nonlinear features and reducing dimensionality.