Semi-supervised learning with graphs
Semi-supervised learning with graphs
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In semi-supervised classification, labels smoothness and cluster assumption are the key point of many successful methods. In graph-based semi-supervised classification, graph representations of the data are quite important. Different graph representations can affect the classification results greatly. Considering the two assumptions and graph representations, we propose a novel method to build a better graph for semi-supervised classification. The graph in our method is called m-step Markov random walk graph (mMRW graph). The smoothness of this graph can be controlled by a parameter m. We believe that a relatively much smoother graph will benefit transductive learning. We also discuss some benefits brought by our smooth graphs. A cluster cohesion based parameter learning method can be efficiently applied to find a proper m. Experiments on artificial and real world dataset indicate that our method has a superior classification accuracy over several state-of-the-art methods.