Summary of Tparafac2: Tracking Evolving Patterns in (incomplete) Temporal Data, by Christos Chatzis et al.
tPARAFAC2: Tracking evolving patterns in (incomplete) temporal data
by Christos Chatzis, Carla Schenker, Max Pfeffer, Evrim Acar
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to tensor factorization that captures evolving patterns in time-evolving datasets. The authors introduce t(emporal)PARAFAC2, which incorporates temporal smoothness regularization to track changes over time. They propose an algorithmic framework using Alternating Optimization (AO) and Alternating Direction Method of Multipliers (ADMM) to fit the model, extending it to handle partially observed data. Experimental results on simulated and real-world datasets demonstrate the effectiveness of this approach, particularly when dealing with missing entries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to find patterns in things that change over time. Right now, we have ways to do this, but they can be limited or hard to use. The authors created a new method called t(emporal)PARAFAC2 that makes it easier to track changes and missing data. They tested it on fake and real datasets and showed how well it works. |
Keywords
* Artificial intelligence * Optimization * Regularization
