Summary of One For All: a Novel Dual-space Co-training Baseline For Large-scale Multi-view Clustering, by Zisen Kong et al.
One for all: A novel Dual-space Co-training baseline for Large-scale Multi-View Clustering
by Zisen Kong, Zhiqiang Fu, Dongxia Chang, Yiming Wang, Yao Zhao
First submitted to arxiv on: 28 Jan 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 proposes a novel multi-view clustering model called Dual-space Co-training Large-scale Multi-view Clustering (DSCMC). The approach aims to enhance clustering performance by leveraging co-training in two distinct spaces. It involves learning a projection matrix for latent consistent anchor graphs from different views and transforming features across multiple views to a shared latent space. The joint optimization of these processes generates a discriminative anchor graph that captures essential characteristics of the multi-view data, facilitating reliable clustering analysis. Additionally, an element-wise method is proposed to mitigate the impact of diverse information between views. DSCMC has approximate linear computational complexity, making it suitable for large-scale datasets. Experimental results demonstrate significant reductions in computational complexity and improved clustering performance compared to existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to group similar things together called multi-view clustering. It’s like grouping people by their interests, favorite foods, or hobbies. The researchers came up with a method that uses two different ways of looking at the data (called “views”) to create a better understanding of how everything is connected. They use special math tricks to make sure all the views are talking to each other correctly and get a clear picture of what’s going on. This helps find groups that fit together well, which is important for lots of applications like customer relationships or disease diagnosis. The method is also fast and can handle big datasets. |
