Perturbative construction of mean-field equations in extensive-rank matrix factorization and denoising

TL;DR

This paper develops a perturbative approach using high-temperature expansions to derive accurate mean-field equations for extensive-rank matrix factorization and denoising, improving upon previous approximations.

Contribution

It introduces a systematic method to compute corrections to existing mean-field approximations for extensive-rank matrix problems using high-temperature expansions.

Findings

Provides closed-form expressions for minimal mean squared error in matrix denoising.

Shows how to incorporate correlation structures for more accurate estimations.

Demonstrates the approach on extensive-rank matrix denoising and compares with optimal estimators.

Abstract

Factorization of matrices where the rank of the two factors diverges linearly with their sizes has many applications in diverse areas such as unsupervised representation learning, dictionary learning or sparse coding. We consider a setting where the two factors are generated from known component-wise independent prior distributions, and the statistician observes a (possibly noisy) component-wise function of their matrix product. In the limit where the dimensions of the matrices tend to infinity, but their ratios remain fixed, we expect to be able to derive closed form expressions for the optimal mean squared error on the estimation of the two factors. However, this remains a very involved mathematical and algorithmic problem. A related, but simpler, problem is extensive-rank matrix denoising, where one aims to reconstruct a matrix with extensive but usually small rank from noisy measurements. In this paper, we approach both these problems using high-temperature expansions at fixed order parameters. This allows to clarify how previous attempts at solving these problems failed at finding an asymptotically exact solution. We provide a systematic way to derive the corrections to these existing approximations, taking into account the structure of correlations particular to the problem. Finally, we illustrate our approach in detail on the case of extensive-rank matrix denoising. We compare our results with known optimal rotationally-invariant estimators, and show how exact asymptotic calculations of the minimal error can be performed using extensive-rank matrix integrals.