RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees

TL;DR

The paper introduces RGNMR, a robust matrix completion algorithm that combines Gauss-Newton linearization with outlier removal, providing theoretical guarantees and superior performance in challenging scenarios.

Contribution

A novel factorization-based RMC method, RGNMR, with proven exact recovery guarantees and improved robustness over existing approaches.

Findings

RGNMR outperforms existing methods in simulations.

It handles fewer observed entries and overparameterization.

It successfully recovers ill-conditioned matrices.

Abstract

Recovering a low rank matrix from a subset of its entries, some of which may be corrupted, is known as the robust matrix completion (RMC) problem. Existing RMC methods have several limitations: they require a relatively large number of observed entries; they may fail under overparametrization, when their assumed rank is higher than the correct one; and many of them fail to recover even mildly ill-conditioned matrices. In this paper we propose a novel RMC method, denoted $\texttt{RGNMR}$, which overcomes these limitations. $\texttt{RGNMR}$ is a simple factorization-based iterative algorithm, which combines a Gauss-Newton linearization with removal of entries suspected to be outliers. On the theoretical front, we prove that under suitable assumptions, $\texttt{RGNMR}$ is guaranteed exact recovery of the underlying low rank matrix. Our theoretical results improve upon the best currently known for factorization-based methods. On the empirical front, we show via several simulations the advantages of $\texttt{RGNMR}$ over existing RMC methods, and in particular its ability to handle a small number of observed entries, overparameterization of the rank and ill-conditioned matrices.