Summary of Multi-view Clustering Integrating Anchor Attribute and Structural Information, by Xuetong Li et al.
Multi-view clustering integrating anchor attribute and structural information
by Xuetong Li, Xiao-Dong Zhang
First submitted to arxiv on: 29 Oct 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 algorithm, AAS, is a novel multi-view clustering approach that leverages both attribute and directed structural information to improve the construction of similarity matrices. By utilizing anchors in each view, AAS integrates this critical information, resulting in more distinct category characteristics. The algorithm’s two-step proximity approach first constructs an anchor structural similarity matrix using strongly connected components of directed graphs, then consolidates the entire process into a unified optimization framework. Experimental results on the modified Attribute SBM dataset demonstrate the effectiveness and superiority of AAS compared to eight existing algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AAS is a new way to group things together based on different types of information. Instead of just looking at what things have in common, it also considers how they are connected. This helps create better groups by considering both what things look like (attributes) and how they’re related (directed graph). The algorithm uses “anchors” to bring this information together, making the groups clearer. Tests show that AAS works well and outperforms other methods. |
Keywords
» Artificial intelligence » Clustering » Optimization
