TY - JOUR
T1 - A Survey of Distributed and Parallel Extreme Learning Machine for Big Data
AU - Wang, Zhiqiong
AU - Sui, Ling
AU - Xin, Junchang
AU - Qu, Luxuan
AU - Yao, Yudong
N1 - Publisher Copyright:
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2020
Y1 - 2020
N2 - Extreme learning machine (ELM) is characterized by good generalization performance, fast training speed and less human intervention. With the explosion of large amount of data generated on the Internet, the learning algorithm in the single-machine environment cannot meet the huge memory consumption of matrix computing, so the implement of distributed ELM algorithm has gradually become one of the research focuses. In view of the research significance and implementation value of distributed ELM, this paperfirst introduced the research background of ELM and improved ELM. Secondly, this paper elaborated the implementation method of distributed ELM from the two directions: Ensemble and matrix operation. Finally, we summarized the development status of distributed ELM and discussed the future research direction.
AB - Extreme learning machine (ELM) is characterized by good generalization performance, fast training speed and less human intervention. With the explosion of large amount of data generated on the Internet, the learning algorithm in the single-machine environment cannot meet the huge memory consumption of matrix computing, so the implement of distributed ELM algorithm has gradually become one of the research focuses. In view of the research significance and implementation value of distributed ELM, this paperfirst introduced the research background of ELM and improved ELM. Secondly, this paper elaborated the implementation method of distributed ELM from the two directions: Ensemble and matrix operation. Finally, we summarized the development status of distributed ELM and discussed the future research direction.
KW - Distributed processing
KW - Ensemble
KW - Extreme learning machine
KW - Matrix operation
UR - http://www.scopus.com/inward/record.url?scp=85102876904&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102876904&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3035398
DO - 10.1109/ACCESS.2020.3035398
M3 - Review article
AN - SCOPUS:85102876904
VL - 8
SP - 201247
EP - 201258
JO - IEEE Access
JF - IEEE Access
ER -