SELF-PACED SUBSPACE CLUSTERING

Subspace clustering aims to segment data sampled from a union of subspaces in visual data tasks. Structured Sparse Subspace Clustering (SSSC) model is a unified optimization framework, which proves successful in learning both the self-representation of the data and their subspace segmentation. However, SSSC involves solving non-convex subproblems and hence it may be stuck into bad local minima such that clustering performance degrades. In this paper, we propose a self-paced subspace clustering algorithm to tackle this problem, which learns subspace segmentation of data by progressing from 'asy' to 'complex' examples under a novel self-paced regularizer. Experiments on the real-world human face datasets verify the effectiveness of the proposed algorithm.