Semisupervised learning using feature selection based on maximum density subgraphs

We present a new graph based semi-supervised learning algorithm, using multiway cut on a neighborhood graph to achieve an optimum classification. We also present a graph based feature selection algorithm utilizing the global structure of the graph derived from both labeled and unlabeled examples. With respect to the experiments we conducted, both of our approaches are proved to have a promising performance on the improvement of the learning accuracy.