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

Zhangquan Wang, Shanshan Huo, Xinlong Xiong, Ke Wang () and Banteng Liu
Additional contact information
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
Banteng Liu: College of Information and Technology, Zhejiang Shuren University, Hangzhou 310015, China

Mathematics, 2023, vol. 11, issue 14, 1-16

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.

Keywords: extreme learning machines; alternating direction method of multipliers; matrix calculation; convex optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/14/3198/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/14/3198/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:14:p:3198-:d:1199126

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().