TY - GEN
T1 - A flexible modeling framework for coupled matrix and tensor factorizations
AU - Evrim, Acar Ataman
AU - Nilsson, Lars Mathias
AU - Saunders, Michael
PY - 2014/11/10
Y1 - 2014/11/10
N2 - 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.
AB - 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.
M3 - Article in proceedings
T3 - Proceedings of the European Signal Processing Conference
SP - 111
EP - 115
BT - Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European
PB - IEEE
T2 - 22nd European Signal Processing Conference
Y2 - 1 September 2014 through 5 September 2014
ER -