LAUSR

Matrix completion aims to find the missing entries from incomplete observations using the low-rank property. Conventional convex optimization based techniques for matrix completion minimize the nuclear norm subject to a constraint on the Frobenius norm of the residual. However, they are not robust to outliers and have a high computational complexity. Different from the existing schemes based on solving a minimization problem, we formulate matrix completion as a feasibility problem. An alternating projection algorithm (APA) is devised to find a feasible point in the intersection of the low-rank constraint set and fidelity constraint set. To achieve resistance to outliers, the fidelity constraint set is modeled as an $\ell _p$ -ball, where the ball center corresponds to the observed data. Furthermore, there is no stepsize within the framework of APA. Convergence of the APA is analyzed and the local linear convergence rate is established. Simulation results demonstrate the efficiency, accuracy, and outlier robustness of the APA.