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Distributed Min-Max Learning Scheme for Neural Networks With Applications to High-Dimensional Classification.

Krishnan Raghavan, Shweta Garg, Sarangapani Jagannathan

    IEEE Transactions on Neural Networks and Learning Systems
    |September 17, 2020
    PubMed
    Summary

    This study introduces a novel distributed learning method for high-dimensional data classification. The approach uses a game theory model to achieve optimal sparsity and improve classifier performance.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Data Science

    Background:

    • High-dimensional data presents significant challenges for traditional classification algorithms.
    • Existing methods often struggle with the curse of dimensionality and feature selection.
    • The need for efficient and scalable classification techniques is critical.

    Purpose of the Study:

    • To introduce a novel learning methodology for classification tasks involving high-dimensional data.
    • To address the challenges posed by high-dimensional datasets by employing a game theory framework.
    • To develop a method for estimating optimal sparsity in deep neural networks.

    Main Methods:

    • Formulation of a L1 regularized zero-sum game between penalty coefficients and deep neural network weights.

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  • Proposal of a distributed learning methodology using additional variables for layerwise cost functions.
  • Application of an alternating minimization approach to find the Nash solution for optimal sparsity.

    • Main Results:

    • The Nash solution effectively provides optimal sparsity and classifier compensation.
    • A novel computational algorithm enables parallel and distributed implementation.
    • Empirical validation on nine datasets demonstrates the approach's efficiency.

    Conclusions:

    • The proposed distributed learning methodology offers an effective solution for high-dimensional data classification.
    • The game theory approach successfully integrates optimal sparsity estimation with deep neural network training.
    • The method is computationally efficient and scalable for practical applications.