Inverse-free extreme learning machine with optimal information updating

Shuai Li, Zhu Hong You, Hongliang Guo, Xin Luo, Zhong Qiu Zhao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

107 Citations (Scopus)


The extreme learning machine (ELM) has drawn insensitive research attentions due to its effectiveness in solving many machine learning problems. However, the matrix inversion operation involved in the algorithm is computational prohibitive and limits the wide applications of ELM in many scenarios. To overcome this problem, in this paper, we propose an inverse-free ELM to incrementally increase the number of hidden nodes, and update the connection weights progressively and optimally. Theoretical analysis proves the monotonic decrease of the training error with the proposed updating procedure and also proves the optimality in every updating step. Extensive numerical experiments show the effectiveness and accuracy of the proposed algorithm.
Original languageEnglish
Article number7115113
Pages (from-to)1229-1241
Number of pages13
JournalIEEE Transactions on Cybernetics
Issue number5
Publication statusPublished - 1 May 2016


  • Extreme learning machine (ELM)
  • inverse-free
  • neural networks
  • optimal updates

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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