An Efficient Kernel Learning Algorithm for Semisupervised Regression Problems

Kernel selection is a central issue in kernel methods of machine learning. In this paper, we investigate the regularized learning schemes based on kernel design methods. Our ideal kernel is derived from a simple iterative procedure using large scale unlabeled data in a semisupervised framework. Compared with most of existing approaches, our algorithm avoids multioptimization in the process of learning kernels and its computation is as efficient as the standard single kernel-based algorithms. Moreover, large amounts of information associated with input space can be exploited, and thus generalization ability is improved accordingly. We provide some theoretical support for the least square cases in our settings; also these advantages are shown by a simulation experiment and a real data analysis.