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    Area of Science:

    • Computer Science
    • Machine Learning
    • Data Mining

    Background:

    • Incomplete multiview clustering (IMVC) typically requires a uniform, manually tuned number of anchors across all views.
    • This constraint limits data diversity and model scalability in IMVC.

    Purpose of the Study:

    • To develop a novel framework, DAQINT, for IMVC that generates differentiated anchor numbers for each view without manual tuning.
    • To improve the scalability and data diversity in IMVC by adaptively weighting predefined anchor numbers.

    Main Methods:

    • DAQINT approximates optimal view-specific anchor numbers by adaptively weighting a predefined set of anchor numbers for each view.
    • It fuses multiscale bipartite graphs using a strategy with linear computation and storage overheads.
    • A three-step iterative algorithm with linear complexity and proven convergence is employed to solve the optimization problem.

    Main Results:

    • DAQINT demonstrates superior performance against several advanced IMVC methods on public datasets.
    • On the Mfeat dataset, DAQINT achieved significant accuracy improvements over competitors like MKC, EEIMVC, FLSD, DSIMVC, IMVC-CBG, and DCP.

    Conclusions:

    • DAQINT effectively addresses the limitations of fixed anchor numbers in IMVC by enabling view-specific anchor counts.
    • The proposed method enhances the exploration of multiview features and balances inter-view importance, leading to state-of-the-art clustering performance.