Robust Low-rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method

TL;DR

This paper introduces a scalable Riemannian optimization approach for robust low-rank matrix completion, decomposing matrices into low-rank and sparse parts, with a novel algorithm and convergence analysis.

Contribution

It formulates RMC as a nonsmooth Riemannian optimization problem on Grassmann manifolds and proposes an alternating manifold proximal gradient continuation method.

Findings

The proposed method outperforms existing approaches on synthetic and real data.

The algorithm has a rigorously analyzed convergence rate.

Numerical experiments demonstrate scalability and effectiveness.

Abstract

Robust low-rank matrix completion (RMC), or robust principal component analysis with partially observed data, has been studied extensively for computer vision, signal processing and machine learning applications. This problem aims to decompose a partially observed matrix into the superposition of a low-rank matrix and a sparse matrix, where the sparse matrix captures the grossly corrupted entries of the matrix. A widely used approach to tackle RMC is to consider a convex formulation, which minimizes the nuclear norm of the low-rank matrix (to promote low-rankness) and the l1 norm of the sparse matrix (to promote sparsity). In this paper, motivated by some recent works on low-rank matrix completion and Riemannian optimization, we formulate this problem as a nonsmooth Riemannian optimization problem over Grassmann manifold. This new formulation is scalable because the low-rank matrix is factorized to the multiplication of two much smaller matrices. We then propose an alternating manifold proximal gradient continuation (AManPGC) method to solve the proposed new formulation. The convergence rate of the proposed algorithm is rigorously analyzed. Numerical results on both synthetic data and real data on background extraction from surveillance videos are reported to demonstrate the advantages of the proposed new formulation and algorithm over several popular existing approaches.