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    This study introduces a new feature selection (FS) framework for unsupervised and semisupervised learning. It effectively combines data structure learning with FS to identify the most informative features for dimensionality reduction.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Artificial Intelligence

    Background:

    • Feature selection (FS) is crucial for dimensionality reduction by identifying informative input features.
    • Existing methods often treat data structure learning and FS separately.

    Purpose of the Study:

    • To propose a novel feature selection framework for unsupervised and semisupervised learning.
    • To unify data structure learning and feature selection in a single formulation for improved performance.

    Main Methods:

    • The framework integrates data structure learning (modeling data distribution) with feature selection.
    • It learns both soft (sample weights) and hard (estimated labels) data structures as regularization terms.
    • An iterative optimization process refines both data structures and feature selection.

    Main Results:

    • The proposed framework leverages the interaction between data structure learning and FS.
    • A new semisupervised feature selection (SSFS) method was derived and analyzed.
    • Experiments on real-world datasets confirmed the effectiveness of the proposed SSFS method.

    Conclusions:

    • The unified framework enhances feature selection by incorporating data distribution insights.
    • The developed SSFS method demonstrates superior performance in identifying discriminative features.
    • This approach offers a powerful tool for dimensionality reduction in various machine learning scenarios.