Summary of Tensor-based Graph Learning with Consistency and Specificity For Multi-view Clustering, by Long Shi and Lei Cao and Yunshan Ye and Yu Zhao and Badong Chen
Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering
by Long Shi, Lei Cao, Yunshan Ye, Yu Zhao, Badong Chen
First submitted to arxiv on: 27 Mar 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 tensor-based multi-view graph learning framework simultaneously considers consistency and specificity, effectively eliminating the influence of noise. The method calculates similarity distance on the Stiefel manifold to preserve intrinsic properties of data. A novel tensor-based target graph learning paradigm is formulated for noise-free graph fusion. The model leverages tensor singular value decomposition (t-SVD) to uncover high-order correlations, enabling a complete understanding of the target graph. An optimization algorithm is derived and experiments on six datasets demonstrate the superiority of the method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to group similar data points together, even when there’s noise in the data. They used special math tools called tensors to make sure their method was good at finding patterns in complex data. The approach considers two types of information: what’s consistent across all the data and what’s unique to each piece. This helps the method avoid mistakes caused by noisy data. The team tested their approach on several datasets and found it worked well. |
Keywords
* Artificial intelligence * Optimization
