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Related Concept Videos

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Related Experiment Video

Updated: Jun 6, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Unifying complete and incomplete multi-view clustering through an information-theoretic generative model.

Yanghang Zheng1, Guoxu Zhou2, Haonan Huang3

  • 1School of Automation, Guangdong University of Technology, Guangzhou, 510006, China; Key Laboratory of Intelligent Information Processing and System Integration of IoT, Ministry of Education, Guangzhou, 510006, China.

Neural Networks : the Official Journal of the International Neural Network Society
|November 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces LOGIC, a novel information-theoretic generative model for unifying complete and incomplete multi-view clustering. LOGIC effectively recovers missing data and enhances clustering performance by considering inter-view and intra-sample relationships.

Keywords:
Contrastive learningIncomplete multi-view clusteringInformation bottleneck theoryMutual information

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Incomplete Multi-View Clustering (IMVC) is crucial due to real-world data limitations.
  • Existing IMVC methods often lack clear recovery explanations and neglect sample relationships.
  • Irrelevant information in existing methods hinders optimal clustering performance.

Purpose of the Study:

  • To unify complete and incomplete multi-view clustering using an information-theoretic generative model.
  • To address limitations in existing IMVC approaches by incorporating sample relationships and reducing irrelevant information.
  • To develop a plug-and-play missing-data recovery module for IMVC.

Main Methods:

  • Proposed LOGIC (Information-theoretiC generative model) based on three information-theoretic principles: comprehensiveness, consensus, and compressibility.
  • Maximized mutual information between common representation and data from each view for missing view recovery.
  • Leveraged consensus principle to uncover associations between different samples by maximizing mutual information between view distributions.
  • Applied compressibility principle to remove task-irrelevant information for effective semantic extraction.

Main Results:

  • Demonstrated LOGIC's effectiveness in generating missing views through extensive empirical studies.
  • Achieved superior performance in clustering tasks compared to state-of-the-art (SOTA) techniques.
  • Showcased consistent improvements in accuracy, normalized mutual information, and purity.

Conclusions:

  • LOGIC effectively unifies complete and incomplete multi-view clustering.
  • The proposed information-theoretic approach enhances both missing data recovery and clustering accuracy.
  • LOGIC offers a versatile solution for IMVC challenges, outperforming existing methods.