正则化超限学习机的多分块松弛交替方向乘子法

doi:10.16451/j.cnki.issn1003-6059.201912006

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Abstract To address the issue of overly heavy computational load of extreme learning machine(ELM) in the big data environment, parallel optimization for ELM is studied. A multi-partition relaxed alternating direction method of multipliers(ADMM) for regularized ELM along with two scalarwise implementations in the N- and N/2-equipartition cases is proposed. By the multi-partition, the proposed algorithm has a highly parallel structure and the combination with relaxation technique improves the convergence rate of the proposed algorithm. Through analysis, a necessary and sufficient convergence condition is established, and optimal convergence ratio and optimal parameters are obtained. Through simulations on bench-mark datasets, the relationship between the convergence ratio and the number of partitioned blocks is calculated, and convergence rates and GPU acceleration ratios of different algorithms are compared. Experimental results demonstrate that the proposed algorithm has low computational complexity and high parallelism.

Key words： Machine Learning Parallel Optimization Extreme Learning Machine Alternating Direction Method of Multipliers Big Data

Received: 08 July 2019

ZTFLH:

TP 18

Fund:Supported by National Natural Science Foundation of China(No.61573123,61503104,U1909209)

Corresponding Authors: LAI Xiaoping, Ph.D., professor. His research interests include optimization method, machine learning and digital filter design.

About author:: ZHANG Lijia, master student. His resear-ch interests include machine learning.Cao Jiuwen, Ph.D., professor. His research interests include machine learning, neural networks and intelligent information processing.

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Cite this article:

ZHANG Lijia,LAI Xiaoping,CAO Jiuwen. Multi-partition Relaxed Alternating Direction Method of Multipliers for Regularized Extreme Learning Machine[J]. , 2019, 32(12): 1107-1115.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201912006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I12/1107

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