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Updated: Nov 29, 2025

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Consensus guided incomplete multi-view spectral clustering.

Jie Wen1, Huijie Sun2, Lunke Fei3

  • 1PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau.

Neural Networks : the Official Journal of the International Neural Network Society
|November 23, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for incomplete multi-view clustering, addressing data with missing views. The consensus guided incomplete multi-view spectral clustering (CGIMVSC) method effectively integrates local and semantic information for better clustering results.

Keywords:
Co-regularizationIncomplete multi-view clusteringManifold learningSpectral clustering

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Area of Science:

  • Machine Learning
  • Data Mining
  • Computer Science

Background:

  • Incomplete multi-view clustering is a challenging problem due to missing data across different views.
  • Existing methods often rely on pre-constructed local structures, which may not be optimal.

Purpose of the Study:

  • To propose a novel method, Consensus Guided Incomplete Multi-View Spectral Clustering (CGIMVSC), for clustering incomplete multi-view data.
  • To effectively explore both local information within individual views and semantic consistency across all views.

Main Methods:

  • CGIMVSC adaptively obtains local structure directly from incomplete data, avoiding k-nearest neighbor pre-construction.
  • A co-regularization constraint is introduced to minimize representational disagreement across views, promoting consensus.
  • The method unifies local and semantic information exploration within a single framework.

Main Results:

  • Experimental comparisons on seven datasets demonstrate the effectiveness of CGIMVSC.
  • The proposed method shows superior performance compared to state-of-the-art approaches for incomplete multi-view clustering.

Conclusions:

  • CGIMVSC offers a robust solution for clustering incomplete multi-view data.
  • The adaptive local structure learning and co-regularization contribute to achieving consensus clustering results.