Summary of Customized Multiple Clustering Via Multi-modal Subspace Proxy Learning, by Jiawei Yao et al.
Customized Multiple Clustering via Multi-Modal Subspace Proxy Learning
by Jiawei Yao, Qi Qian, Juhua Hu
First submitted to arxiv on: 6 Nov 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 novel end-to-end multiple clustering approach, Multi-Sub, is introduced to flexibly adapt to diverse user-specific needs in data grouping. By incorporating a multi-modal subspace proxy learning framework, Multi-Sub aligns textual prompts expressing user preferences with their corresponding visual representations. This allows for the customized representation of data in terms specific to the user’s interests. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multiple clustering aims to discover various latent structures of data from different aspects. A new approach, called Multi-Sub, is designed to address limitations in existing works that struggle to adapt to diverse user-specific needs. The method uses a combination of CLIP and GPT-4 to generate proxy words from large language models that act as subspace bases. |
Keywords
» Artificial intelligence » Clustering » Gpt » Multi modal
