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    This study introduces a novel multiview structural large margin classifier (MvSLMC) to effectively utilize information from multiple feature sets. The proposed method enhances computational efficiency and classifier diversity for improved multiview learning outcomes.

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    Area of Science:

    • Machine Learning
    • Computer Science
    • Data Science

    Background:

    • Multiview learning (MVL) leverages multiple feature sets per instance, but exploring common and complementary information is challenging.
    • Existing pairwise strategies in MVL are computationally expensive and limit inter-view relationship exploration.

    Purpose of the Study:

    • To propose a novel multiview structural large margin classifier (MvSLMC) that addresses limitations in current MVL algorithms.
    • To simultaneously satisfy consensus and complementarity principles across all views in MVL.

    Main Methods:

    • MvSLMC employs structural regularization for within-class cohesion and between-class separability.
    • It utilizes inter-view structural information to enhance classifier diversity.
    • A safe screening rule (SSR) is introduced for computational acceleration, leveraging sample sparsity from hinge loss.

    Main Results:

    • The proposed MvSLMC effectively integrates information from multiple views.
    • The safe screening rule significantly accelerates MvSLMC computation.
    • Numerical experiments confirm the effectiveness of both MvSLMC and its acceleration method.

    Conclusions:

    • MvSLMC offers an effective approach to multiview learning by capturing consensus and complementarity.
    • The introduction of safe screening provides a significant computational advantage for MVL.
    • This work presents the first safe screening method for multiview learning problems.