A Survey of Distributed and Parallel Extreme Learning Machine for Big Data

被引:5
|
作者
Wang, Zhiqiong [1 ,4 ,5 ]
Sui, Ling [2 ]
Xin, Junchang [2 ]
Qu, Luxuan [1 ]
Yao, Yudong [3 ]
机构
[1] Northeastern Univ, Coll Med & Biol Informat Engn, Shenyang 110819, Peoples R China
[2] Northeastern Univ, Sch Comp Sci & Engn, Key Lab Big Data Management & Analyt Liaoning, Shenyang 110819, Peoples R China
[3] Stevens Inst Technol, Dept Eletr & Comp Engn, Hoboken, NJ 07030 USA
[4] Neusoft Res Intelligent Healthcare Technol Co Ltd, Shenyang 110179, Peoples R China
[5] Harbin Engn Univ, Acousit Sci & Technol Lab, Harbin 0086, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Extreme learning machine; distributed processing; ensemble; matrix operation; ELM; CLASSIFICATION; REGRESSION; FRAMEWORK; MAPREDUCE; ALGORITHM; TRENDS; ROBUST;
D O I
10.1109/ACCESS.2020.3035398
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 paper first 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.
引用
收藏
页码:201247 / 201258
页数:12
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