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    This study enhances multiview clustering (MVC) by integrating a min-max learning paradigm into late fusion MVC (LF-MVC). The novel approach significantly improves clustering accuracy and reduces computation time compared to existing methods.

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Artificial Intelligence

    Background:

    • Multiview clustering (MVC) leverages diverse data sources for improved performance.
    • Simple multiple kernel k-means (SimpleMKKM) utilizes a min-max formulation for effective clustering.
    • Late fusion MVC (LF-MVC) combines information from multiple views at a later stage.

    Purpose of the Study:

    • To integrate the min-max learning paradigm into late fusion MVC (LF-MVC).
    • To address the challenges of a tri-level max-min-max optimization problem in LF-MVC.
    • To enhance clustering accuracy and computational efficiency in multiview data analysis.

    Main Methods:

    • Developed a novel LF-MVC algorithm incorporating a min-max learning paradigm.
    • Designed an efficient two-step alternative optimization strategy to solve the complex optimization problem.
    • Conducted theoretical analysis of the algorithm's generalization clustering performance.

    Main Results:

    • The proposed algorithm significantly reduces computation time.
    • Achieved improved clustering accuracy (ACC) compared to state-of-the-art LF-MVC algorithms.
    • Demonstrated effective convergence and stable learning of consensus clustering matrices.

    Conclusions:

    • The integration of min-max learning enhances LF-MVC performance.
    • The proposed algorithm offers a computationally efficient and accurate solution for multiview clustering.
    • The study provides theoretical insights and empirical validation for the novel approach.