Label propagation through sparse neighborhood and its applications

In this paper, a novel semi-supervised learning approach is proposed. It assumes that, for the ith sample x _i, the samples from x _i's sparse neighborhood have the same label with x _i and the label of x _i can be linearly reconstructed by the labels of those samples from x _i's sparse neighborhood. Our algorithm firstly selects the sparse neighborhood for each sample, and then in that sparse neighborhood finds the sparse coefficients to represent the local geometry structure, finally seeks a label propagation way. Different from many existing methods, we construct the adapting graph, simultaneously, give the weight of each edge. What's more, we highlight the role of those samples in that sparse neighborhood, meanwhile, eliminate the role of those samples out of that sparse neighborhood. The experimental results on face recognition and document classification demonstrate the effectiveness and efficiency of our proposed approach in this paper.