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cs.LG2025
PARAFAC2-based Coupled Matrix and Tensor Factorizations with Constraints
Carla Schenker, Xiulin Wang, David Horner +2
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 C…
cs.LG2025
tPARAFAC2: Tracking evolving patterns in (incomplete) temporal data
Christos Chatzis, Carla Schenker, Max Pfeffer +1
Tensor factorizations have been widely used for the task of uncovering patterns in various domains. Often, the input is time-evolving, shifting the goal to tracking the evolution o…
cs.LG2025
dCMF: Learning interpretable evolving patterns from temporal multiway data
Christos Chatzis, Carla Schenker, Jérémy E. Cohen +1
Multiway datasets are commonly analyzed using unsupervised matrix and tensor factorization methods to reveal underlying patterns. Frequently, such datasets include timestamps and c…