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September 20, 2025

Consensus-Guided Incomplete Multi-view Clustering via Cross-view Affinities Learning

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Authors

QLQian LiuHWHuibing WangJPJinjia Peng

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Overview

The proposed approach improves clustering performance in high-dimension data, addressing issues of outliers and representation via consensus learning.

Key Points

  • CAL demonstrates significant improvements in clustering performance over traditional methods, focusing on multi-view scenarios.
  • The method implements a structured sparse constraint to refine representation tensors, reducing bias from noisy data.
  • CAL strategically reconstructs views to capture sample-wise affinities, enhancing data quality and clustering results.
  • Cross-view affinities allow for better semantic correlation understanding, leading to a unified structural graph in multiple views.

Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68d46fcd31b076d99fa69d8dhttps://doi.org/10.24963/ijcai.2025/641
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