Summary of Self-supervised Graph Embedding Clustering, by Fangfang Li et al.
Self-Supervised Graph Embedding Clustering
by Fangfang Li, Quanxue Gao, Cheng Deng, Wei Xia
First submitted to arxiv on: 24 Sep 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 The proposed self-supervised graph embedding framework integrates manifold learning with K-means to address limitations in traditional K-means dimensionality reduction. By connecting K-means to the manifold structure, centroids are no longer required, allowing for one-step clustering without redundant balancing hyperparameters. This approach naturally maintains class balance through _{2,1}-norm maximization, a theoretically proven result. The framework is evaluated on multiple datasets, demonstrating excellent and reliable performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new method combines K-means and manifold learning to make clustering easier and more accurate. It’s like having a map to help find the right groups of things. This approach gets rid of the need for special starting points (centroids) and makes sure that the groups are balanced. The results show that this method works well on different datasets. |
Keywords
» Artificial intelligence » Clustering » Dimensionality reduction » Embedding » K means » Manifold learning » Self supervised
