In this work, we first consider a general formulation and provide a unified theoretical guarantee of the block diagonal property of the solutions.
We propose a novel convex relaxation of SSC based on the convex hull of the fixed rank projection matrices.
We introduce a regularized MRW learning model, using a low-rank penalty to constrain the global subspace structure, for subspace clustering and estimation.