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Related Experiment Video

Updated: Feb 19, 2026

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Enhancing Multi-View Clustering: A Sufficient Information-Theoretic Approach for Consistency Acquisition and

Yazhou Ren, Zichen Wen, Junlong Ke

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    Sufficient Multi-View Clustering (STMVC) uses information theory to learn consistent data patterns while removing redundancy across views. This novel approach enhances clustering performance and adapts to single-view and incomplete data scenarios.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Information Theory

    Background:

    • Multi-view clustering (MVC) leverages diverse data sources for improved performance.
    • Existing MVC methods often overlook redundant information across views, potentially limiting effectiveness.

    Purpose of the Study:

    • To propose Sufficient Multi-View Clustering (STMVC), an information-theoretic framework for MVC.
    • To learn inter-view consistency while removing redundant information among views.
    • To enhance adaptability for single-view and incomplete multi-view scenarios.

    Main Methods:

    • Utilizes variational analysis to extract inter-view consistency information.
    • Introduces a sufficient representation lower bound to enhance consistency and minimize redundancy.
    • Extends the framework to single-view and incomplete multi-view settings.

    Main Results:

    • Demonstrates outstanding performance on multi-view and single-view datasets.
    • Theoretical analysis validates the model using the Bayesian error rate.
    • STMVC effectively learns inter-view consistency and reduces redundancy.

    Conclusions:

    • STMVC offers a novel perspective for multi-view data analysis.
    • The method provides a promising solution for the challenges in multi-view clustering.
    • STMVC shows strong adaptability and generalizability across different data scenarios.