SPARSE SUBSPACE CLUSTERING WITH SEQUENTIALLY ORDERED AND WEIGHTED L1-MINIMIZATION

Abstract

Built on the sparse representation framework, sparse subspace clustering (SSC) received considerable attention in the recent years. Conventional SSC employs. l(1)-minimization based sparse regression for neighbor identification on a sample-by-sample basis, and is unaware of the neighbor information revealed by those already computed sparse representation vectors. To rid this drawback, this paper proposes a weighted. l(1)-minimization based sparse regression method, and an associated data ordering rule able to reflect the reliability of neighbor information for further enhancing the clustering accuracy. The selection of weighting coefficients for SSC is also discussed. Computer simulations using both the synthesis and real data are provided to evidence the effectiveness of the proposed method.