Summary of Interpretable Multi-view Clustering Based on Anchor Graph Tensor Factorization, by Rui Wang et al.
Interpretable Multi-View Clustering Based on Anchor Graph Tensor Factorization
by Rui Wang, Jing Li, Quanxue Gao, Cheng Deng
First submitted to arxiv on: 1 Apr 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 clustering method leverages the power of non-negative matrix factorization (NMF) to obtain cluster label matrices from anchor graphs, showcasing exceptional performance on large-scale data sets. This approach bypasses traditional post-processing requirements, making it a viable alternative for bipartite graph-based methods. The paper introduces an innovative framework that combines multiple views using non-negative tensor factorization, allowing for comprehensive consideration of inter-view information and enhanced interpretability through the decomposition of sample and anchor indicator tensors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a new way to group similar things together based on connections between them. They use a special kind of math called matrix factorization to find patterns in the data. This helps them identify clusters or groups that are meaningful and easy to understand. The method is useful for big datasets and can even handle multiple types of information at once, which makes it very powerful. The results show that this approach works really well and can be used to make discoveries in many different fields. |
Keywords
* Artificial intelligence * Clustering
