Data Based Construction of Kernels for Semi-Supervised Learning With Less Labels

Abstract

This paper deals with the problem of semi-supervised learning using a small number of training samples. Traditional kernel based methods utilize either a fixed kernel or a combination of judiciously chosen kernels from a fixed dictionary. In contrast, we construct a data-dependent kernel utilizing the Mercer components of different kernels constructed using ideas from diffusion geometry, and use a regularization technique with this kernel with adaptively chosen parameters. Our algorithm is illustrated using a few well-known data sets as well as a data set for automatic gender identification. For some of these data sets, we obtain a zero test error using only a minimal number of training samples.