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Cross-Modal Multivariate Pattern Analysis
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Dual-Structural Bipartite Graph Learning for Multiview Clustering.

Xiaohui Wei, Haibo Liu, Puhong Duan

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    This study introduces Dual-Structural Bipartite Graph Learning (DsBiGL) for multiview clustering. DsBiGL enhances clustering by optimizing view-specific anchors and shared bipartite graphs using novel structural constraints.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Graph Theory

    Background:

    • Bipartite graphs (BiG) are effective for massive multiview data clustering.
    • Regulating structural information in view-specific anchors and shared BiGs requires further investigation.

    Purpose of the Study:

    • To propose a novel Dual-Structural BiG Learning (DsBiGL) method.
    • To address the challenge of regulating structural information in multiview bipartite graph learning.

    Main Methods:

    • DsBiGL transforms BiG learning into a joint optimization of IntrA-view and InteR-view Subspace Learning (IASL and IRSL).
    • Employs k-nearest neighbor (KNN) and low-rank constraints for view-specific anchors (IASL) and shared BiGs (IRSL).
    • Integrates IASL and IRSL into a unified model for interactive enhancement and uses an iterative optimization algorithm.

    Main Results:

    • Experimental results on diverse multiview datasets show DsBiGL outperforms comparative methods.
    • Demonstrates the superiority of DsBiGL in achieving accurate clustering results.

    Conclusions:

    • DsBiGL effectively enhances view-specific anchor representation and view-shared BiG learning.
    • The proposed method offers a significant advancement in multiview data clustering using bipartite graphs.