Efficient Matrix Completion with Gaussian Models

TL;DR

This paper introduces a Gaussian model-based framework with a MAP-EM algorithm for matrix completion, achieving comparable results to state-of-the-art methods with reduced computational cost.

Contribution

It presents a novel probabilistic approach using Gaussian models and MAP-EM for efficient matrix completion, demonstrating competitive performance.

Findings

Achieves similar accuracy to state-of-the-art methods

Reduces computational cost significantly

Effective on challenging movie ratings datasets

Abstract

A general framework based on Gaussian models and a MAP-EM algorithm is introduced in this paper for solving matrix/table completion problems. The numerical experiments with the standard and challenging movie ratings data show that the proposed approach, based on probably one of the simplest probabilistic models, leads to the results in the same ballpark as the state-of-the-art, at a lower computational cost.