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    Extreme Learning Machines (ELM) face computational challenges with big data. A new method, maximally split and relaxed ADMM (MS-RADMM), significantly speeds up regularized ELM training by splitting computations.

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    Area of Science:

    • Machine Learning
    • Computational Science

    Background:

    • Extreme Learning Machines (ELM) offer fast learning but struggle with high-dimensional big data.
    • Computational load remains a significant bottleneck for ELM in big data scenarios.

    Purpose of the Study:

    • To develop an efficient computational method for regularized Extreme Learning Machines (RELM) in big data environments.
    • To address the computational burden of ELM by leveraging advanced optimization techniques.

    Main Methods:

    • Developed a novel maximally split and relaxed Alternating Direction Method of Multipliers (MS-RADMM) for RELM.
    • Incorporated a scalarwise implementation and a relaxation technique for enhanced parallelism.
    • Established convergence conditions and analyzed convergence rates of the proposed MS-RADMM.

    Main Results:

    • MS-RADMM demonstrates linear convergence with a faster rate than unrelaxed methods.
    • Experimental results on benchmark datasets confirm the method's fast convergence and parallelism.
    • Optimal parameter values and a fast parameter selection scheme were identified.

    Conclusions:

    • MS-RADMM effectively reduces the computational load of RELM in big data settings.
    • The proposed method offers significant advantages in terms of speed and parallel processing.
    • MS-RADMM provides a viable and efficient solution for training ELMs on large datasets.