Sub-Graph Regularization for Scalable Semi-supervised Classification

During the past decade, graph-based semi-supervised learning has become one of the most important research areas in machine learning and artificial intelligence community. In this paper, we propose a sub-graph to construct the graph for semi-supervised learning (SSL). The new graph is scalable so that it can be extended to large-scale data. Based on this graph, we then propose a sub-graph regularization for scalable SSL. It can also project the new-coming data to infer its label for handling out-of-sample problem. Simulation results show that the proposed method can achieve better performance compared with other state-of-the-art graph based SSL methods.