Improving Semi-Supervised Target Alignment via Label-Aware Base Kernels

Abstract

Semi-supervised kernel design is an essential step for obtaining good predictive performance in semi-supervised learning tasks. In the current literatures, a large family of algorithms builds the new kernel by using the weighted average of predefined base kernels. While optimal weighting schemes have been studied extensively, the choice of base kernels received much less attention. Many methods simply adopt the empirical kernel matrices or its eigenvectors. Such base kernels are computed irrespective of class labels and may not always reflect useful structures in the data. As a result, in case of poor base kernels, the generalization performance can be degraded however hard their weights are tuned. In this paper, we propose to construct high-quality base kernels with the help of label information to globally improve the final target alignment. In particular, we devise label-aware kernel eigenvectors under the framework of semi-supervised eigenfunction extrapolation, which span base kernels that are more useful for learning. Such base kernels are individually better aligned to the learning target, so their mixture will more likely generate a good classifier. Our approach is computationally efficient, and demonstrates encouraging performance in semi-supervised classification and regression.