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

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

20 Citeringar (Scopus)


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.
Referentgranskad vetenskaplig tidskriftNeurocomputing
Volym174, Part A
Sidor (från-till)18-30
Antal sidor13
StatusPublicerad - 22.01.2016
MoE-publikationstypA1 Originalartikel i en vetenskaplig tidskrift


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