An Algorithm of Schatten p-norm Regularized Least Squares Problems for Video Restoration

摘要

Minimizing the nuclear norm is recently considered as the convex relaxation of the rank minimization problem and arises in many applications as Netflix challenge. A closest nonconvex relaxation - Schatten p(0 < p < 1) norm minimization has been proposed to replace the NP hard rank minimization. In this paper, an algorithm based on Majorization Minimization has be proposed to solve Schatten p(0 < p < 1) norm minimization. The numerical experiments show that Schatten p norm with 0 < p < 1 recovers low rank matrix from fewer measurements than nuclear norm minimization. The numerical results also indicate that our algorithm give a more accurate reconstruction.