Summary of Parafac2-based Coupled Matrix and Tensor Factorizations with Constraints, by Carla Schenker et al.
PARAFAC2-based Coupled Matrix and Tensor Factorizations with Constraints
by Carla Schenker, Xiulin Wang, David Horner, Morten A. Rasmussen, Evrim Acar
First submitted to arxiv on: 18 Jun 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 In this paper, researchers introduce a new algorithmic framework for Coupled Matrix and Tensor Factorizations (CMTF) models based on PARAFAC2 tensor models. These models are useful for analyzing data from multiple sources with irregular or dynamic time profiles. The proposed framework uses Alternating Optimization (AO) and the Alternating Direction Method of Multipliers (ADMM) to fit PARAFAC2-based CMTF models while allowing for various constraints and couplings between datasets. Experimental results demonstrate the framework’s utility, versatility, and benefits in terms of accuracy and efficiency compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to analyze data from multiple sources with irregular or dynamic time profiles. The method uses a type of tensor model called PARAFAC2, which is good at handling this kind of data. The researchers created an algorithm that can fit these models while allowing for different types of constraints and connections between the datasets. This makes it more flexible and powerful than other methods. |
Keywords
* Artificial intelligence * Optimization
