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    We developed a discriminative ridge machine (DRM) for supervised classification. This novel approach enhances existing regression models by incorporating class discriminativeness, achieving superior performance on diverse datasets.

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    Area of Science:

    • Machine Learning
    • Statistical Modeling
    • Supervised Classification

    Background:

    • Existing regression models like ridge, lasso, and group lasso lack explicit class discriminativeness.
    • Accurate derivation of categorical information requires models that account for inter-class differences.

    Purpose of the Study:

    • Introduce a novel discriminative ridge regression approach for supervised classification.
    • Develop a quadratic model, the discriminative ridge machine (DRM), with a closed-form solution.
    • Propose iterative algorithms to improve DRM efficiency and scalability.

    Main Methods:

    • Developed a discriminative ridge regression framework.
    • Formulated a special case as a quadratic model (DRM) with an analytical solution.
    • Implemented three iterative algorithms for DRM optimization.
    • Applied the approach to various data types including images, high-dimensional, and imbalanced data.

    Main Results:

    • The discriminative ridge machine (DRM) demonstrated superior performance compared to state-of-the-art classifiers.
    • Extensive experimental results validated the effectiveness of the proposed discriminative approach.
    • The DRM successfully derived categorical information by accounting for class discriminativeness.

    Conclusions:

    • The discriminative ridge regression approach, particularly the DRM, offers a powerful tool for supervised classification.
    • The proposed methods are versatile and applicable to a wide range of data types and real-world applications.
    • DRM provides an effective way to enhance classification accuracy by integrating discriminative information into regression models.