Summary of Concrete Dense Network For Long-sequence Time Series Clustering, by Redemptor Jr Laceda Taloma et al.
Concrete Dense Network for Long-Sequence Time Series Clustering
by Redemptor Jr Laceda Taloma, Patrizio Pisani, Danilo Comminiello
First submitted to arxiv on: 8 May 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 addresses the challenge of learning cluster-friendly representations for long and complex time series data, a fundamental task in time series clustering. Recent advancements have relied on surrogate losses to integrate canonical k-means into neural networks, leading to sub-optimal solutions. The authors propose LoSTer, a novel dense autoencoder architecture that optimizes the k-means objective using the Gumbel-softmax reparameterization trick. This approach is designed for accurate and fast clustering of long time series data. The paper presents extensive experiments on benchmark datasets and real-world applications, demonstrating the effectiveness of LoSTer over state-of-the-art RNNs and Transformer-based deep clustering methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to group similar patterns in a long list of numbers that change over time. This is called time series clustering, and it’s important for discovering hidden patterns. Right now, there are many ways to do this, but they’re not very good at handling really long lists of numbers. The authors of this paper have created a new way to group these numbers, called LoSTer, that can handle even the longest lists quickly and accurately. They tested it on lots of different datasets and real-world examples, and it did much better than other methods. This is important because it could help us make discoveries in many fields, like finance or healthcare. |
Keywords
» Artificial intelligence » Autoencoder » Clustering » K means » Softmax » Time series » Transformer
