Summary of An Adaptive Framework For Multi-view Clustering Leveraging Conditional Entropy Optimization, by Lijian Li
An Adaptive Framework for Multi-View Clustering Leveraging Conditional Entropy Optimization
by Lijian Li
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 proposes a novel framework called CE-MVC, which addresses challenges in multi-view clustering (MVC) by integrating an adaptive weighting algorithm with a parameter-decoupled deep model. The approach leverages conditional entropy and normalized mutual information to quantify the informative contribution of each view and construct robust unified representations. CE-MVC outperforms existing methods in experiments, offering a more accurate solution for MVC tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CE-MVC is a new way to group data from different sources together, making it easier to find patterns and connections. The method helps reduce the negative effects of noisy or bad data and produces better results than current approaches. |
Keywords
» Artificial intelligence » Clustering
