Data fusion models based on Coupled Matrix and Tensor Factorizations (CMTF)
have been effective tools for joint analysis of data from multiple sources.
While the vast majority of CMTF models are based on the strictly multilinear
CANDECOMP/PARAFAC (CP) tensor model, recently also the more flexible PARAFAC2
model has been integrated into CMTF models. PARAFAC2 tensor models can handle
irregular/ragged tensors and have shown to be especially useful for modelling
dynamic data with unaligned or irregular time profiles. However, existing
PARAFAC2-based CMTF models have limitations in terms of possible
regularizations on the factors and/or types of coupling between datasets. To
address these limitations, in this paper we introduce a flexible algorithmic
framework that fits PARAFAC2-based CMTF models using Alternating Optimization
(AO) and the Alternating Direction Method of Multipliers (ADMM). The proposed
framework allows to impose various constraints on all modes and linear
couplings to other matrix-, CP- or PARAFAC2-models. Experiments on various
simulated and a real dataset demonstrate the utility and versatility of the
proposed framework as well as its benefits in terms of accuracy and efficiency
in comparison with state-of-the-art methods.

基于耦合矩阵和张量分解的数据融合模型是从多个数据源联合分析数据的有效工具。本文介绍了一种灵活的算法框架，利用交替优化和交替方向乘子法来适应基于 PARAFAC2 的 CMTF 模型，允许在所有模式和线性耦合到其他矩阵、CP 或 PARAFAC2 模型上施加各种约束。实验证明了该框架的实用性和多功能性，并与最先进的方法相比，在准确性和效率方面具有优势。