Spectral Transformation Approaches to Semi-supervised Learning

A foundational problem in kernel-based semi-supervised learning is the design of suitable kernels which can properly reflect the underlying data manifold. One of the most well-known semi-supervised kernel learning approaches is the spectral kernel learning methodology which usually tunes the spectra of the graph Laplacian empirically or through optimizing some generalized performance measures. In this study, we proposed a novel approach to do spectral kernel learning based on maximum margin criterion, which is theoretically justified as a more essential semi-supervised kernel learning measure than others, such as kernel target alignment. We have conducted lots of experiments on public data sets, showing promising performance of our scheme.