2 citations · 2 across the 1 of their papers we have counts for
3 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…
A Time-aware tensor decomposition for tracking evolving patterns
Christos Chatzis, Max Pfeffer, Pedro Lind +1
Time-evolving data sets can often be arranged as a higher-order tensor with one of the modes being the time mode. While tensor factorizations have been successfully used to capture…