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

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  • Jiuwen Cao
  • Zhiping Lin

Abstract

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.

Suggested Citation

  • Jiuwen Cao & Zhiping Lin, 2015. "Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-13, July.
    • Handle: RePEc:hin:jnlmpe:103796
    DOI: 10.1155/2015/103796
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