A Brain Network Inspired Algorithm: Pre-trained Extreme Learning Machine

  • Yongshan Zhang
  • Jia Wu
  • Zhihua CaiEmail author
  • Siwei Jiang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)


Extreme learning machine (ELM) is a promising learning method for training “generalized” single hidden layer feedforward neural networks (SLFNs), which has attracted significant interest recently for its fast learning speed, good generalization ability and ease of implementation. However, due to its manually selected network parameters (e.g., the input weights and hidden biases), the performance of ELM may be easily deteriorated. In this paper, we propose a novel pre-trained extreme learning machine (P-ELM for short) for classification problems. In P-ELM, the superior network parameters are pre-trained by an ELM-based autoencoder (ELM-AE) and embedded with the underlying data information, which can improve the performance of the proposed method. Experiments and comparisons on face image recognition and handwritten image annotation applications demonstrate that P-ELM is promising and achieves superior results compared to the original ELM algorithm and other ELM-based algorithms.


Extreme learning machine ELM-based autoencoder Pre-trained parameter Classification 



This work is supported in part by the National Nature Science Foundation of China (Grant Nos. 61403351 and 61773355), the Key Project of the Natural Science Foundation of Hubei Province, China (Grant No. 2013CFA004), the National Scholarship for Building High Level Universities, China Scholarship Council (No. 201706410005), and the Self-Determined and Innovative Research Founds of CUG (No. 1610491T05).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yongshan Zhang
    • 1
  • Jia Wu
    • 2
  • Zhihua Cai
    • 1
    Email author
  • Siwei Jiang
    • 1
  1. 1.Department of Computer ScienceChina University of GeosciencesWuhanChina
  2. 2.Department of Computing, Faculty of Science and EngineeringMacquarie UniversitySydneyAustralia

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