Summary of Fast Disentangled Slim Tensor Learning For Multi-view Clustering, by Deng Xu et al.
Fast Disentangled Slim Tensor Learning for Multi-view Clustering
by Deng Xu, Chao Zhang, Zechao Li, Chunlin Chen, Huaxiong Li
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A recently proposed method for multi-view clustering, fast Disentangled Slim Tensor Learning (DSTL), addresses limitations in existing approaches. DSTL directly explores high-order correlations among multi-view latent semantic representations using matrix factorization. The model disentangles the representation into a semantic-unrelated part and a semantic-related part for each view, alleviating feature redundancy. Two slim tensors are constructed with tensor-based regularization to further enhance feature disentanglement. The alignment of semantic-related representations across views is facilitated through a consensus alignment indicator. DSTL’s computational efficiency makes it effective in solving clustering tasks. Experimental results demonstrate the superiority and efficiency of DSTL compared to state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to group similar things from multiple sources, called multi-view clustering, has been developed. This method is called fast Disentangled Slim Tensor Learning (DSTL). It improves on previous methods by directly looking at how different pieces of information relate to each other. DSTL takes out extra information that’s not important and aligns the remaining information across all sources. This makes it better than existing methods at solving this type of problem. |
Keywords
» Artificial intelligence » Alignment » Clustering » Regularization
