Matrix Completion over Finite Fields: Bounds and Belief Propagation Algorithms

TL;DR

This paper advances the theory and algorithms for low rank matrix completion over finite fields, introducing a new graphical model and belief propagation method that outperform previous approaches in efficiency and accuracy.

Contribution

It provides the first belief propagation algorithm for finite field matrix completion of arbitrary size and improves theoretical guarantees for existing algorithms.

Findings

Reduced computational complexity from O(n^{2r+3}) to O(n^2)

Enhanced performance over previous algorithms

First graphical model for finite field matrix completion

Abstract

We consider the low rank matrix completion problem over finite fields. This problem has been extensively studied in the domain of real/complex numbers, however, to the best of authors' knowledge, there exists merely one efficient algorithm to tackle the problem in the binary field, due to Saunderson et al. [1]. In this paper, we improve upon the theoretical guarantees for the algorithm provided in [1]. Furthermore, we formulate a new graphical model for the matrix completion problem over the finite field of size $q$, $\Bbb{F}_q$, and present a message passing (MP) based approach to solve this problem. The proposed algorithm is the first one for the considered matrix completion problem over finite fields of arbitrary size. Our proposed method has a significantly lower computational complexity, reducing it from $O(n^{2r+3})$ in [1] down to $O(n^2)$ (where, the underlying matrix has dimension $n \times n$ and $r$ denotes its rank), while also improving the performance.