Robust PCA via non-convex half-quadratic regularization

Zhi-Yong Wang, Xiao Peng Li, Hing Cheung So, Zhaofeng Liu

Abstract: Highlights•A new non-convex regularization is utilized to attain robustness and sparseness, and its proximity operator is derived, which makes the corresponding optimization problem solvable in an efficient manner.•We theoretically analyze that any limit point of the iterates is a critical point of the objective function.•There are no tunable parameters other than the termination conditions in the proposed algorithm and experiments demonstrate that our method exhibits better restoration results, compared with the competing approaches.