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    Area of Science:

    • Machine Learning
    • Data Mining
    • Computer Science

    Background:

    • Multiple Kernel Clustering (MKC) optimally combines base kernels for improved clustering.
    • Existing MKC algorithms struggle with incomplete kernel matrices lacking rows or columns.

    Purpose of the Study:

    • To develop novel MKC algorithms capable of handling incomplete kernel matrices.
    • To integrate imputation and clustering into a unified framework for enhanced performance.

    Main Methods:

    • Proposed two algorithms: one unifying imputation and clustering, another enhancing mutual completion of kernels.
    • Developed a three-step iterative algorithm to solve optimization problems.
    • Theoretically analyzed the generalization bounds of the proposed algorithms.

    Main Results:

    • Algorithms consistently outperform existing imputation-based methods across 13 benchmark datasets.
    • Performance improvement increases with higher missing data ratios.
    • Demonstrated effectiveness and advantages of joint imputation and clustering.

    Conclusions:

    • The proposed MKC algorithms effectively address incomplete kernel matrices.
    • Joint imputation and clustering offers a superior approach compared to traditional methods.
    • The algorithms show significant promise for real-world applications with missing data.