Summary of Manifold-based Incomplete Multi-view Clustering Via Bi-consistency Guidance, by Huibing Wang et al.
Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance
by Huibing Wang, Mingze Yao, Yawei Chen, Yunqiu Xu, Haipeng Liu, Wei Jia, Xianping Fu, Yang Wang
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The proposed Manifold-based Incomplete Multi-view clustering via Bi-consistency guidance (MIMB) method addresses the limitations of existing incomplete multi-view clustering methods by flexibly recovering incomplete data among various views. MIMB adds reconstruction terms to representation learning, dynamically examining the latent consensus representation and introducing an adaptive weight term for each view. The method also incorporates a biconsistency guidance strategy with reverse regularization of the consensus representation and proposes a manifold embedding measure for exploring the hidden structure of the recovered data. Experimental results on 6 benchmark datasets demonstrate MIMB’s superiority over several state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MIMB is a new way to group things together, called clustering, when some information is missing. Current methods try to fill in the missing parts, but this can cause mistakes and make it harder to get good results. The authors created MIMB to fix these problems. It works by looking at different views of the same thing (like pictures from different angles) and trying to find a balance between all the views. They also developed a special way to look at the data, called manifold embedding, to understand the hidden patterns. This helps MIMB group things correctly. The authors tested MIMB on several real-world datasets and showed that it performs better than other methods. |
Keywords
» Artificial intelligence » Clustering » Embedding » Regularization » Representation learning
