Subspace clustering

We proposed a tensor based low-rank representation for subspace clustering.

We propose choosing the sparest solution in the solution set.

We study the robust subspace clustering problem, and present a general framework from the viewpoint of half-quadratic optimization to unify the L1 norm, Frobenius norm, L21 norm and nuclear norm based subspace clustering methods.

We propose linearized alternating direction method with parallel splitting and adaptive penalty for efficiently solving linearly constrained multi-variable separable convex programs, which are abundant in machine learning.

we propose a novel objective function named Low-Rank Representation (LRR), which seeks the lowest rank representation among all the candidates that can represent the data samples as linear combinations of the bases in a given dictionary.