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Spectral Embedding Fusion for Incomplete Multiview Clustering.

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    This study introduces a unified spectral embedding tensor learning (USETL) framework for incomplete multiview clustering (IMVC). USETL effectively integrates spectral embedding fusion and tensor learning to handle missing data and enhance clustering performance.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Vision

    Background:

    • Incomplete multiview clustering (IMVC) seeks to uncover data structure in datasets with missing information across multiple views.
    • Existing graph-based IMVC methods leverage low-rank tensor learning for high-order information but often neglect spectral embedding fusion and struggle with missing data.
    • Addressing missing instances and features remains a significant challenge for current IMVC techniques.

    Purpose of the Study:

    • To propose a novel Unified Spectral Embedding Tensor Learning (USETL) framework for Incomplete Multiview Clustering (IMVC).
    • To integrate spectral embedding fusion of multiple similarity graphs with spectral embedding tensor learning within a single framework.
    • To effectively address the challenges of missing instances and features in multiview data.

    Main Methods:

    • Developed a USETL framework that combines spectral embedding fusion and spectral embedding tensor learning for IMVC.
    • Implemented spectral embedding fusion using spectral rotations at both the feature and clustering indicator levels to reduce redundancy.
    • Employed spectral embedding tensor learning to capture consistent and complementary information via high-order correlations among multiple views.
    • Utilized a strategy of removing missing instances to construct similarity graphs for incomplete multiview data.

    Main Results:

    • The USETL framework demonstrated effectiveness in handling incomplete multiview data.
    • Spectral embedding fusion at two distinct data levels successfully removed redundant information.
    • The proposed method effectively captured both consistent and complementary information across multiple views.
    • Experimental results on various multiview datasets validated the efficacy of the USETL framework.

    Conclusions:

    • The USETL framework offers a robust solution for incomplete multiview clustering.
    • Integrating spectral embedding fusion and tensor learning significantly improves IMVC performance.
    • The proposed approach provides an effective strategy for managing missing data in multiview clustering tasks.