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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 103796, 13 pages
Review Article

Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey

1Key Lab for IOT and Information Fusion Technology of Zhejiang, Hangzhou Dianzi University, Zhejiang 310018, China
2School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 643798

Received 12 November 2014; Accepted 21 March 2015

Academic Editor: Roman Lewandowski

Copyright © 2015 Jiuwen Cao and Zhiping Lin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Extreme learning machine (ELM) has been developed for single hidden layer feedforward neural networks (SLFNs). In ELM algorithm, the connections between the input layer and the hidden neurons are randomly assigned and remain unchanged during the learning process. The output connections are then tuned via minimizing the cost function through a linear system. The computational burden of ELM has been significantly reduced as the only cost is solving a linear system. The low computational complexity attracted a great deal of attention from the research community, especially for high dimensional and large data applications. This paper provides an up-to-date survey on the recent developments of ELM and its applications in high dimensional and large data. Comprehensive reviews on image processing, video processing, medical signal processing, and other popular large data applications with ELM are presented in the paper.