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    This study introduces a new Cluster-weighted mUlti-view infoRmation bottlEneck (CURE) algorithm for multi-view clustering. CURE effectively learns cluster-wise weights, improving data analysis by better utilizing complementary information across different data views.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Vision

    Background:

    • Weighted multi-view clustering (MVC) integrates complementary information from multi-view data for consistent clustering.
    • Existing MVC methods struggle with vastly different cluster-wise weights, limiting their ability to leverage complementary information due to view-wise weight learning.
    • Current approaches often require difficult-to-tune parameters for weight distribution control.

    Purpose of the Study:

    • To propose a novel Cluster-weighted mUlti-view infoRmation bottlEneck (CURE) algorithm for enhanced multi-view clustering.
    • To automatically learn cluster-wise weights, improving the exploitation of cluster-level complementary information.
    • To overcome limitations of existing methods in handling varying cluster-wise weights and parameter tuning.

    Main Methods:

    • Developed a new weight learning scheme based on the relationship between mutual information of joint cluster distributions and cluster weights.
    • Introduced a novel draw-and-merge method to efficiently solve the optimization problem.
    • Implemented the Cluster-weighted mUlti-view infoRmation bottlEneck (CURE) algorithm for automatic cluster-wise weight discovery.

    Main Results:

    • Experimental results demonstrate the superiority of the proposed CURE algorithm on various multi-view datasets.
    • The CURE algorithm effectively learns cluster-wise weights, enhancing clustering performance.
    • The method successfully exploits cluster-level complementary information, outperforming state-of-the-art approaches.

    Conclusions:

    • The proposed Cluster-weighted mUlti-view infoRmation bottlEneck (CURE) algorithm offers a superior approach to weighted multi-view clustering.
    • Automatic learning of cluster-wise weights is crucial for effectively utilizing complementary information in multi-view data.
    • CURE provides an effective and robust solution for multi-view clustering tasks, particularly when dealing with heterogeneous data views.