Kernel selection forl semi-supervised kernel machines

Guang Dai, Dit-Yan Yeung

Abstract: Existing semi-supervised learning methods are mostly based on either the cluster assumption or the manifold assumption. In this paper, we propose an integrated regularization framework for semi-supervised kernel machines by incorporating both the cluster assumption and the manifold assumption. Moreover, it supports kernel learning in the form of kernel selection. The optimization problem involves joint optimization over all the labeled and unlabeled data points, a convex set of basic kernels, and a discrete space of unknown labels for the unlabeled data. When the manifold assumption is incorporated, graph Laplacian kernels are used as the basic kernels for learning an optimal convex combination of graph Laplacian kernels. Comparison with related methods on the USPS data set shows very promising results.