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Human Collective Intelligence Inspired Multi-View Representation Learning - Enabling View Communication by Simulating

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    This study introduces view communication for multi-view representation learning, enhancing data analysis. The novel approach improves classification accuracy across medicine, bioinformatics, and machine learning fields.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Artificial Intelligence

    Background:

    • Multi-view learning utilizes multiple data sources for enhanced decision-making.
    • Multi-view representation learning is crucial for integrating diverse data.
    • Improving multi-view representation learning performance is an ongoing challenge.

    Purpose of the Study:

    • To introduce a novel multi-view representation learning approach inspired by human collective intelligence.
    • To enhance information exploitation and mutual assistance between data views.

    Main Methods:

    • Proposed a novel multi-view representation learning approach incorporating view communication.
    • Simulated human communication mechanisms for multi-round view interactions.
    • Enabled each view to leverage complementary information from other views.

    Main Results:

    • Achieved substantial improvements in average classification accuracy.
    • Demonstrated a 4.536% increase in medicine and bioinformatics.
    • Showed a 4.115% increase in machine learning applications.

    Conclusions:

    • The proposed view communication approach significantly enhances multi-view representation learning.
    • The method effectively utilizes complementary information for improved model performance.
    • Results validate the approach's effectiveness across diverse scientific fields.