An Acceleration Technique for Matrix Completion using Deep Unfolding

Abstract

This paper proposes an acceleration technique using deep unfolding for a matrix completion problem (MCP), which is a problem of estimating missing entries of a matrix. Various algorithms have been proposed for this problem, and their recovery performances depend on parameters used in the algorithms. This paper focuses on the alternating gradient descent (AGD) algorithm for the MCP and shows that its performance depends on step size parameters. Then the deep unfolding is applied to the algorithm and provides a trainable AGD (TAGD) algorithm. Numerical examples show that TAGD algorithm achieves better performance than AGD algorithm.