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This is a Experimental Study study.

September 20, 2025

LRGR: Self-Supervised Incomplete Multi-View Clustering via Local Refinement and Global Realignment

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Authors

YXYanwanyu XiQufu Normal University XZXiao ZhengHubei University of Technology CTChang TangHuazhong University of Science and Technology

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Implication

Observational analysis demonstrates improvements in representation learning for incomplete multi-view clustering, suggesting enhanced model performance.

Key Points

The LRGR model improves the clarity of clustering by refining imputed representations, which mitigates distribution shifts and allows better group separation.
Key experiments show competitive performance across five multi-view datasets, indicating the effectiveness of local refinement and global realignment techniques.
Utilizing a local imputed refinement module, the model enhances representation versatility through cross-view contrastive learning.
The global realignment stage promotes semantic consistency using pseudo-labels, addressing some common pitfalls in typical GCN approaches.

Cite This Study

Xi et al. (2025) studied this question.

synapsesocial.com/papers/68d469d631b076d99fa6704b https://doi.org/10.24963/ijcai.2025/737

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Implication

Observational analysis demonstrates improvements in representation learning for incomplete multi-view clustering, suggesting enhanced model performance.

LRGR: Self-Supervised Incomplete Multi-View Clustering via Local Refinement and Global Realignment

Discussion

Member takes

Implication

Key Points

Cite This Study

LRGR: Self-Supervised Incomplete Multi-View Clustering via Local Refinement and Global Realignment

Discussion

Member takes

Implication

Key Points

Cite This Study

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