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    Area of Science:

    • Neuroimaging
    • Machine Learning
    • Medical Diagnostics

    Background:

    • High-dimensional data and limited samples pose challenges in computer-aided Alzheimer's disease (AD) diagnosis.
    • Feature selection and subspace learning are key strategies to address these challenges.
    • Existing methods often focus on binary classification, limiting broader application.

    Purpose of the Study:

    • To develop a unified framework for discriminative feature selection in AD diagnosis.
    • To combine the interpretability of feature selection with the performance of subspace learning.
    • To enable multiclass classification for more comprehensive AD diagnosis.

    Main Methods:

    • Utilized Linear Discriminant Analysis (LDA) and Locality Preserving Projection (LPP) for subspace learning.
    • Integrated LDA and LPP into a unified framework for discriminative feature selection.
    • Applied the method to multiclass classification tasks in AD diagnosis.

    Main Results:

    • The proposed method effectively selects class-discriminative and noise-resistant features.
    • Demonstrated superior performance compared to state-of-the-art methods in experiments.
    • Successfully applied to multiclass classification on the Alzheimer's Disease Neuroimaging Initiative dataset.

    Conclusions:

    • The combined approach offers a robust solution for feature selection in AD diagnosis.
    • The method enhances the accuracy and applicability of computer-aided AD diagnosis.
    • This framework advances the potential for improved diagnostic tools in neurodegenerative diseases.