A brain network inspired algorithm: Pre-trained Extreme Learning Machine

Yongshan Zhang, Jia Wu, Zhihua Cai*, Siwei Jiang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationNeural information processing
Subtitle of host publication24th International Conference, ICONIP 2017, Guangzhou, China, November 14–18, 2017, Proceedings, Part V
EditorsDerong Liu, Shengli Xie, Yuanqing Li, Dongbin Zhao, El-Sayed M. El-Alfy
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Number of pages10
ISBN (Electronic)9783319701394
ISBN (Print)9783319701387
Publication statusPublished - 2017
Event24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China
Duration: 14 Nov 201718 Nov 2017

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on Neural Information Processing, ICONIP 2017

Bibliographical note

An errata exists for this publication and the original paper has been corrected. The erratum can be found at doi: 10.1007/978-3-319-70139-4_94


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


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