Manifold learning in local tangent space via extreme learning machine

Qian Wang, Weiguo Wang, Rui Nian, Bo He, Yue Shen, Kaj-Mikael Björk, Amaury Lendasse

Research output: Contribution to journalArticleScientificpeer-review

17 Citations (Scopus)

Abstract

In this paper, we propose a fast manifold learning strategy to estimate the underlying geometrical distribution and develop the relevant mathematical criterion on the basis of the extreme learning machine (ELM) in the high-dimensional space. The local tangent space alignment (LTSA) method has been used to perform the manifold production and the single hidden layer feedforward network (SLFN) is established via ELM to simulate the low-dimensional representation process. The scheme of the ELM ensemble then combines the individual SLFN for the model selection, where the manifold regularization mechanism has been brought into ELM to preserve the local geometrical structure of LTSA. Some developments have been done to evaluate the inherent representation embedding in the ELM learning. The simulation results have shown the excellent performance in the accuracy and efficiency of the developed approach.
Original languageEnglish
Peer-reviewed scientific journalNeurocomputing
Volume174, Part A
Issue numberJanuary
Pages (from-to)18-30
Number of pages13
ISSN0925-2312
DOIs
Publication statusPublished - 22.01.2016
MoE publication typeA1 Journal article - refereed

Keywords

  • 512 Business and Management
  • Extreme learningmachine
  • Manifold learning
  • Local tangentspacealignment
  • High-dimensional space

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