Semi-supervised Learning via Bipartite Graph Construction with Adaptive Neighbors

Graph-based semi-supervised learning, which further utilizes graph structure behind samples for boosting semi-supervised learning, gains convincing results in several machine learning tasks. Nevertheless, existing graph-based methods have shortcomings from two aspects. On the one hand, many of them concentrate on improving label propagation over the constructed graph through time-saving methods, e.g. path searching, without giving insights on constructing a proper graph accommodated to samples. On the other hand, some models are only devoted to constructing the appropriate graph resulting in a two-stage procedure, which may incur a suboptimal scenario. In this paper, we develop a joint learning method that considers both bipartite graph construction and label propagation simultaneously. With this configuration, the constructed graph is constantly adjusted by the smoothness term in the objective as the algorithm proceeds. The time complexity of our method gets significant improvement compared with traditional graph-based methods, and the experimental results on one synthetic dataset and several real-world benchmarks demonstrate the effectiveness and scalability of our proposed method.