Home Page

Papers

Submissions

News

Editorial Board

Proceedings

Open Source Software

Search

Statistics

Login

Frequently Asked Questions

Contact Us



RSS Feed

Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion

Dimitris Bertsimas, Michael Lingzhi Li; 21(231):1−43, 2020.

Abstract

We formulate the problem of matrix completion with and without side information as a non-convex optimization problem. We design fastImpute based on non-convex gradient descent and show it converges to a global minimum that is guaranteed to recover closely the underlying matrix while it scales to matrices of sizes beyond $10^5 \times 10^5$. We report experiments on both synthetic and real-world datasets that show fastImpute is competitive in both the accuracy of the matrix recovered and the time needed across all cases. Furthermore, when a high number of entries are missing, fastImpute is over $75\%$ lower in MAPE and $15$ times faster than current state-of-the-art matrix completion methods in both the case with side information and without.

[abs][pdf][bib]       
© JMLR 2020. (edit, beta)