Non-Negative Matrix Factorization Using Non-Von Neumann Computers

TL;DR

This paper explores solving non-negative matrix factorization (NMF) using non-von Neumann architectures, specifically the Dirac-3 device, demonstrating promising preliminary results that outperform traditional solvers in certain cases.

Contribution

It introduces a novel approach to NMF leveraging energy-based optimization on non-von Neumann hardware, with formulations suitable for such devices and initial experimental validation.

Findings

Dirac-3 outperforms CP-SAT in serial processing for integer matrices.

Fusion of Dirac-3 results with Scikit-learn's NMF improves reconstruction error.

Preliminary results suggest potential advantages of non-von Neumann architectures for NMF.

Abstract

Non-negative matrix factorization (NMF) is a matrix decomposition problem with applications in unsupervised learning. The general form of this problem (along with many of its variants) is NP-hard in nature. In our work, we explore how this problem could be solved with an energy-based optimization method suitable for certain machines with non-von Neumann architectures. We used the Dirac-3, a device based on the entropy computing paradigm and made by Quantum Computing Inc., to evaluate our approach. Our formulations consist of (i) a quadratic unconstrained binary optimization model (QUBO, suitable for Ising machines) and a quartic formulation that allows for real-valued and integer variables (suitable for machines like the Dirac-3). Although current devices cannot solve large NMF problems, the results of our preliminary experiments are promising enough to warrant further research. For non-negative real matrices, we observed that a fusion approach of first using Dirac-3 and then feeding its results as the initial factor matrices to Scikit-learn's NMF procedure outperforms Scikit-learn's NMF procedure on its own, with default parameters in terms of the error in the reconstructed matrices. For our experiments on non-negative integer matrices, we compared the Dirac-3 device to Google's CP-SAT solver (inside the Or-Tools package) and found that for serial processing, Dirac-3 outperforms CP-SAT in a majority of the cases. We believe that future work in this area might be able to identify domains and variants of the problem where entropy computing (and other non-von Neumann architectures) could offer a clear advantage.