29 citations · 76 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
PARAFAC2-based Coupled Matrix and Tensor Factorizations
Carla Schenker, Xiulin Wang, Evrim Acar
Coupled matrix and tensor factorizations (CMTF) have emerged as an effective data fusion tool to jointly analyze data sets in the form of matrices and higher-order tensors. The PAR…
An AO-ADMM approach to constraining PARAFAC2 on all modes
Marie Roald, Carla Schenker, Vince D. Calhoun +4
Analyzing multi-way measurements with variations across one mode of the dataset is a challenge in various fields including data mining, neuroscience and chemometrics. For example,…
PARAFAC2 AO-ADMM: Constraints in all modes
Marie Roald, Carla Schenker, Jeremy E. Cohen +1
The PARAFAC2 model provides a flexible alternative to the popular CANDECOMP/PARAFAC (CP) model for tensor decompositions. Unlike CP, PARAFAC2 allows factor matrices in one mode (i.…
A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings
Carla Schenker, Jeremy E. Cohen, Evrim Acar
Coupled matrix and tensor factorizations (CMTF) are frequently used to jointly analyze data from multiple sources, also called data fusion. However, different characteristics of da…