Sparse subspace clustering via Low-Rank structure propagation

Highlights：

• A novel data sample representation is proposed, where a low-rank structure is propagated in the sparse representation. Based on the representation, a self-expression is constructed for subspace clustering. Experimental results on synthesis datasets and several real datasets demonstrate that the proposed subspace clustering algorithm performs comparably against state-of-the-art methods.

• A theoretical proof is provided to show the proposed approach can reveal the true membership of the data samples being clustered.

• Discussions from both geometric and physical perspectives on the proposed approach are made as an explanation and an algorithm analysis.