Subspace Clustering with Sparsity and Grouping Effect

Subspace clustering aims to group a set of data from a union of subspaces into the subspace from which it was drawn. It has become a popularmethod for recovering the low-dimensional structure underlying high-dimensional dataset. The state-of-the-art methods construct an affinity matrix based on the self-representation of the dataset and then use a spectral clustering method to obtain the final clustering result. These methods show that sparsity and grouping effect of the affinity matrix are important in recovering the low-dimensional structure. In this work, we propose a weighted sparse penalty and a weighted grouping effect penalty in modeling the self-representation of data points. The experimental results on Extended Yale B, USPS, and Berkeley 500 image segmentation datasets showthat the proposedmodel ismore effective than state-of-the-artmethods in revealing the subspace structure underlying high-dimensional dataset.