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A Maximally Split and Adaptive Relaxed Alternating Direction Method of Multipliers for Regularized Extreme Learning Machines

Author

Listed:
  • Zhangquan Wang

    (College of Information and Technology, Zhejiang Shuren University, Hangzhou 310015, China)

  • Shanshan Huo

    (School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China)

  • Xinlong Xiong

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310014, China)

  • Ke Wang

    (College of Information and Technology, Zhejiang Shuren University, Hangzhou 310015, China
    State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China)

  • Banteng Liu

    (College of Information and Technology, Zhejiang Shuren University, Hangzhou 310015, China)

Abstract

One of the significant features of extreme learning machines (ELMs) is their fast convergence. However, in the big data environment, the ELM based on the Moore–Penrose matrix inverse still suffers from excessive calculation loads. Leveraging the decomposability of the alternating direction method of multipliers (ADMM), a convex model-fitting problem can be split into a set of sub-problems which can be executed in parallel. Using a maximally splitting technique and a relaxation technique, the sub-problems can be split into multiple univariate sub-problems. On this basis, we propose an adaptive parameter selection method that automatically tunes the key algorithm parameters during training. To confirm the effectiveness of this algorithm, experiments are conducted on eight classification datasets. We have verified the effectiveness of this algorithm in terms of the number of iterations, computation time, and acceleration ratios. The results show that the method proposed by this paper can greatly improve the speed of data processing while increasing the parallelism.

Suggested Citation

  • Zhangquan Wang & Shanshan Huo & Xinlong Xiong & Ke Wang & Banteng Liu, 2023. "A Maximally Split and Adaptive Relaxed Alternating Direction Method of Multipliers for Regularized Extreme Learning Machines," Mathematics, MDPI, vol. 11(14), pages 1-16, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3198-:d:1199126
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    References listed on IDEAS

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    1. Min Li & Defeng Sun & Kim-Chuan Toh, 2015. "A Convergent 3-Block Semi-Proximal ADMM for Convex Minimization Problems with One Strongly Convex Block," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 32(04), pages 1-19.
    2. Deren Han & Xiaoming Yuan & Wenxing Zhang & Xingju Cai, 2013. "An ADM-based splitting method for separable convex programming," Computational Optimization and Applications, Springer, vol. 54(2), pages 343-369, March.
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    Cited by:

    1. Ke Wang & Shanshan Huo & Banteng Liu & Zhangquan Wang & Tiaojuan Ren, 2023. "An Adaptive Low Computational Cost Alternating Direction Method of Multiplier for RELM Large-Scale Distributed Optimization," Mathematics, MDPI, vol. 12(1), pages 1-20, December.

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