Summary of Discovering Common Information in Multi-view Data, by Qi Zhang et al.
Discovering Common Information in Multi-view Data
by Qi Zhang, Mingfei Lu, Shujian Yu, Jingmin Xin, Badong Chen
First submitted to arxiv on: 21 Jun 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 paper introduces a novel definition for computing common information from multi-view data, inspired by Gács-Körner common information in information theory. A supervised multi-view learning framework is developed to capture both common and unique information, leveraging the new definition. The framework minimizes a total correlation term to ensure independence between the extracted common and unique information. To estimate information-theoretic quantities, matrix-based R{é}nyi’s -order entropy functional is employed, which eliminates the need for variational approximation and distributional estimation in high-dimensional space. Theoretical proof shows that the framework can accurately discover both common and unique information from multi-view data. Experimental results on synthetic and seven real-world datasets demonstrate the superior performance of the proposed framework compared to state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to understand what’s shared between different types of data, like images or words. They create a formula that helps computers learn about both common and unique patterns in these data sets. This is important because it means computers can better understand and make use of this information. The authors test their approach on many real-world datasets and show that it performs better than other methods. |
Keywords
» Artificial intelligence » Supervised
