Summary of Tpch: Tensor-interacted Projection and Cooperative Hashing For Multi-view Clustering, by Zhongwen Wang et al.
TPCH: Tensor-interacted Projection and Cooperative Hashing for Multi-view Clustering
by Zhongwen Wang, Xingfeng Li, Yinghui Sun, Quansen Sun, Yuan Sun, Han Ling, Jian Dai, Zhenwen Ren
First submitted to arxiv on: 25 Dec 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 This paper introduces Tensor-Interacted Projection and Cooperative Hashing for Multi-View Clustering (TPCH), a novel approach that tackles the limitations of existing anchor and hash-based multi-view clustering methods. These limitations arise from neglecting higher-order interactions among data views during projection, leading to poor quality hash representations, clustering performance, and noise sensitivity. TPCH addresses this issue by stacking multiple projection matrices into a tensor, capturing synergies and communications between views. The method employs an enhanced tensor nuclear norm to learn more compact and distinguishable hash representations, promoting communication within and between views. Experimental results demonstrate significant improvements over state-of-the-art methods on five large-scale multi-view datasets in terms of clustering performance and CPU time efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving how computers cluster groups of data from different sources together. When we have lots of data, it’s hard to make sure that the clusters are good because current methods don’t take into account how the different types of data relate to each other. This new method, called TPCH, does a better job of considering these relationships and makes more accurate clusters. It also runs faster than previous methods, which is important when working with large amounts of data. |
Keywords
» Artificial intelligence » Clustering
