Summary of How to Characterize Imprecision in Multi-view Clustering?, by Jinyi Xu et al.
How to characterize imprecision in multi-view clustering?
by Jinyi Xu, Zuowei Zhang, Ze Lin, Yixiang Chen, Zhe Liu, Weiping Ding
First submitted to arxiv on: 7 Apr 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 paper proposes a novel approach for clustering multi-view data called Multi-View Low-Rank Evidential C-Means (MvLRECM). Current methods can only assign objects to specific clusters, failing to capture imprecision in overlapping regions. MvLRECM addresses this issue by allowing each object to belong to multiple clusters with varying degrees of support, characterizing uncertainty when making decisions. The proposed method also introduces meta-clusters for objects in overlapping regions, reducing imprecision and improving accuracy through entropy-weighting and low-rank constraints. Experimental results on toy and real-world datasets demonstrate the effectiveness of MvLRECM compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are trying to figure out how to group different types of data together correctly, but it’s hard because some objects can belong to multiple groups at once. The new method called MvLRECM tries to solve this problem by giving each object a degree of support for being in different groups. This helps when making decisions and makes the results more accurate. It also reduces errors that happen when data points are close to multiple group boundaries. The team tested their method on some sample datasets and real-world data, and it performed better than other methods. |
Keywords
* Artificial intelligence * Clustering
