<ul data-eligibleForWebStory="true"><li>Data fusion models based on Coupled Matrix and Tensor Factorizations (CMTF) have been effective tools for joint analysis of data from multiple sources.</li><li>Recent advancements have integrated the more flexible PARAFAC2 model into CMTF models, enabling handling of irregular/ragged tensors and dynamic data with unaligned time profiles.</li><li>Existing PARAFAC2-based CMTF models have limitations in terms of possible regularizations and types of data coupling.</li><li>To address these limitations, a new algorithmic framework has been introduced in this paper for fitting PARAFAC2-based CMTF models using Alternating Optimization (AO) and the Alternating Direction Method of Multipliers (ADMM).</li><li>The proposed framework allows for imposing various constraints on all modes and linear couplings to other matrix-, CP- or PARAFAC2-models.</li><li>Experiments on simulated and real datasets have shown the utility and versatility of the proposed framework, highlighting its accuracy and efficiency compared to state-of-the-art methods.</li></ul>

PARAFAC2-based Coupled Matrix and Tensor Factorizations with Constraints

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