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MSDLSR: Margin Scalable Discriminative Least Squares Regression for Multicategory Classification.

Lingfeng Wang, Xu-Yao Zhang, Chunhong Pan

    IEEE Transactions on Neural Networks and Learning Systems
    |October 7, 2015
    PubMed
    Summary

    We introduce a new Margin Scalable Discriminative Least Squares Regression (MSDLSR) model for multicategory classification. This method enhances control over the classification margin, outperforming existing approaches in experiments.

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    Area of Science:

    • Machine Learning
    • Computer Science
    • Data Science

    Background:

    • Least Squares Regression (LSR) and L2-Support Vector Machines (L2-SVM) are foundational in classification.
    • Existing Discriminative Least Squares Regression (DLSR) models offer a relaxation of L2-SVM but lack explicit margin control.
    • Controlling the margin is crucial for robust classification performance and generalization.

    Purpose of the Study:

    • To propose a novel Margin Scalable Discriminative Least Squares Regression (MSDLSR) model.
    • To explicitly control and enhance the margin of DLSR models for multicategory classification.
    • To theoretically analyze the margin determination and support vectors within the MSDLSR framework.

    Main Methods:

    • The study establishes DLSR as a relaxation of L2-SVM.
    • A theorem on DLSR margin is derived, enabling explicit margin control.
    • The MSDLSR model is developed by adding a constraint to DLSR to restrict zero dragging values, thereby controlling the margin.

    Main Results:

    • The MSDLSR model theoretically analyzes margin determination and support vectors.
    • Extensive experiments demonstrate superior performance of MSDLSR.
    • The proposed method outperforms current state-of-the-art approaches on diverse datasets.

    Conclusions:

    • MSDLSR offers improved multicategory classification by explicitly controlling the margin.
    • The theoretical underpinnings of MSDLSR provide insights into margin determination and support vector behavior.
    • The model's effectiveness is validated through comprehensive experimental results on various machine learning and real-world datasets.