Summary of Dynamic Multi-network Mining Of Tensor Time Series, by Kohei Obata et al.
Dynamic Multi-Network Mining of Tensor Time Series
by Kohei Obata, Koki Kawabata, Yasuko Matsubara, Yasushi Sakurai
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
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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 proposes a novel method, Dynamic Multi-network Mining (DMM), for subsequence clustering of tensor time series, which enables interpretable and accurate discovery of clusters in high-dimensional tensors. The DMM approach converts the tensor time series into segment groups with varying lengths, characterized by sparse dependency networks constrained with l1-norm. This method provides interpretable insights into key relationships by characterizing each cluster with multiple networks, one for each non-temporal mode. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For curious learners or general audiences, this paper is about a new way to group similar patterns in large collections of data that change over time. The goal is to find meaningful groups within these patterns and understand what they mean. To achieve this, the researchers developed a method called Dynamic Multi-network Mining (DMM) that works well with high-dimensional data. |
Keywords
* Artificial intelligence * Clustering * Time series
