Summary of A Survey on Deep Clustering: From the Prior Perspective, by Yiding Lu et al.
A Survey on Deep Clustering: From the Prior Perspective
by Yiding Lu, Haobin Li, Yunfan Li, Yijie Lin, Xi Peng
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 comprehensive review of deep clustering methods, highlighting their reliance on incorporating and utilizing prior knowledge. The authors categorize these methods into six types based on different prior knowledge sources, showing that most follow two trends: from mining to constructing and from internal to external. A benchmark is provided for five widely-used datasets, analyzing the performance of various methods with diverse priors. This work aims to provide novel insights and inspire future research in the deep clustering community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a type of machine learning called deep clustering, which helps us understand complex data. Right now, there are many ways to do this, but they all rely on using some prior knowledge or assumption about the data. The authors group these methods into categories based on what kind of prior knowledge is being used, and show that most follow a few patterns. They also test different methods on five datasets and see how well they work. This helps us understand which methods are best for certain types of data. |
Keywords
» Artificial intelligence » Clustering » Machine learning
