Analysis of Spectral Kernel Design based Semi-supervised Learning

We consider a framework for semi-supervised learning using spectral decomposition based un-supervised kernel design. This approach subsumes a class of previously proposed semi-supervised learning methods on data graphs. We examine various theoretical properties of such methods. In particular, we derive a generalization performance bound, and obtain the optimal kernel design by minimizing the bound. Based on the theoretical analysis, we are able...