Combining Smooth Graphs with Semi-supervised Learning

Abstract

The key points of the semi-supervised learning problem are the label smoothness and cluster assumptions. In graph-based semi-supervised learning, graph representations of the data are so important that different graph representations can affect the classification results heavily. We present a novel method to produce a graph called smooth Markov random walk graph which takes into account the two assumptions employed by semi-supervised learning. The new graph is achieved by modifying the eigenspectrum of the transition matrix of Markov random walk graph and is sufficiently smooth with respect to the intrinsic structure of labeled and unlabeled points. We believe the smoother graph will benefit semi-supervised learning. Experiments on artificial and real world dataset indicate that our method provides superior classification accuracy over several state-of-the-art methods.