2011/05/17 by Evrim Acar, Tamara G. Kolda, Acar, Evrim +3 · 11 citations
Mathematics · #Data Analysis #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Machine Learning (stat.ML) #Numerical Analysis (math.NA) #Statistics and Probability (physics.data-an) #Tensor decomposition and applications
paper · pdf · doi:10.48550/arxiv.1105.3422
openalex publication_date 2011/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.