Robust rank constrained sparse learning: A graph-based method for clustering

Graph-based clustering is an advanced clustering techniuqe, which partitions the data according to an affinity graph. However, the graph quality affects the clustering results to a large extent, and it is difficult to construct a graph with high quality, especially for data with noises and outliers. To solve this problem, a robust rank constrained sparse learning method is proposed in this paper. The L_2,1-norm objective function of sparse representation is introduced to learn the optimal graph with robustness. To preserve the data structure, the graph is searched within the neighborhood of the initial graph. By incorporating a rank constraint, the learned graph can be directly used as the cluster indicator and the final results is obtained without additional post-processing. Plenty of experiments on real-world data sets have proved the superiority and the robustness of the proposed approach.