A flexible modeling framework for coupled matrix and tensor factorizations. Acar, E., Nilsson, M., & Saunders, M. In 2014 22nd European Signal Processing Conference (EUSIPCO), pages 111-115, Sep., 2014.
A flexible modeling framework for coupled matrix and tensor factorizations [pdf]Paper  abstract   bibtex   
Joint analysis of data from multiple sources has proved useful in many disciplines including metabolomics and social network analysis. However, data fusion remains a challenging task in need of data mining tools that can capture the underlying structures from multi-relational and heterogeneous data sources. In order to address this challenge, data fusion has been formulated as a coupled matrix and tensor factorization (CMTF) problem. Coupled factorization problems have commonly been solved using alternating methods and, recently, unconstrained all-at-once optimization algorithms. In this paper, unlike previous studies, in order to have a flexible modeling framework, we use a general-purpose optimization solver that solves for all factor matrices simultaneously and is capable of handling additional linear/nonlinear constraints with a nonlinear objective function. We formulate CMTF as a constrained optimization problem and develop accurate models more robust to overfactoring. The effectiveness of the proposed modeling/algorithmic framework is demonstrated on simulated and real data.

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