  Coupled matrix and tensor factorizations (CMTF) are frequently used to
jointly analyze data from multiple sources, also called data fusion. However,
different characteristics of datasets stemming from multiple sources pose many
challenges in data fusion and require to employ various regularizations,
constraints, loss functions and different types of coupling structures between
datasets. In this paper, we propose a flexible algorithmic framework for
coupled matrix and tensor factorizations which utilizes Alternating
Optimization (AO) and the Alternating Direction Method of Multipliers (ADMM).
The framework facilitates the use of a variety of constraints, loss functions
and couplings with linear transformations in a seamless way. Numerical
experiments on simulated and real datasets demonstrate that the proposed
approach is accurate, and computationally efficient with comparable or better
performance than available CMTF methods for Frobenius norm loss, while being
more flexible. Using Kullback-Leibler divergence on count data, we demonstrate
that the algorithm yields accurate results also for other loss functions.
