All-at-once Optimization for Coupled Matrix and Tensor Factorizations

TL;DR

This paper introduces CMTF-OPT, a gradient-based all-at-once optimization method for coupled matrix and tensor factorization, improving accuracy in joint analysis of heterogeneous, incomplete data sets.

Contribution

It proposes a novel all-at-once optimization algorithm for coupled matrix and tensor factorization, outperforming traditional alternating algorithms.

Findings

CMTF-OPT achieves higher accuracy than alternating least squares.

The method effectively handles incomplete and heterogeneous data.

Numerical experiments validate the improved performance.

Abstract

Joint analysis of data from multiple sources has the potential to improve our understanding of the underlying structures in complex data sets. For instance, in restaurant recommendation systems, recommendations can be based on rating histories of customers. In addition to rating histories, customers' social networks (e.g., Facebook friendships) and restaurant categories information (e.g., Thai or Italian) can also be used to make better recommendations. The task of fusing data, however, is challenging since data sets can be incomplete and heterogeneous, i.e., data consist of both matrices, e.g., the person by person social network matrix or the restaurant by category matrix, and higher-order tensors, e.g., the "ratings" tensor of the form restaurant by meal by person. In this paper, we are particularly interested in fusing data sets with the goal of capturing their underlying latent structures. We formulate this problem as a coupled matrix and tensor factorization (CMTF) problem where heterogeneous data sets are modeled by fitting outer-product models to higher-order tensors and matrices in a coupled manner. Unlike traditional approaches solving this problem using alternating algorithms, we propose an all-at-once optimization approach called CMTF-OPT (CMTF-OPTimization), which is a gradient-based optimization approach for joint analysis of matrices and higher-order tensors. We also extend the algorithm to handle coupled incomplete data sets. Using numerical experiments, we demonstrate that the proposed all-at-once approach is more accurate than the alternating least squares approach.