Release Time:2019-03-09  Hits:

Indexed by: Journal Article

Date of Publication: 2009-08-01

Journal: NEUROCOMPUTING

Included Journals: Scopus、SCIE、EI

Volume: 72

Issue: 13-15

Page Number: 3066-3076

ISSN: 0925-2312

Key Words: Extreme learning machine; Lanczos bidiagonalization; Singular value decomposition; Regularization; Generalized cross validation

Abstract: There are two problems preventing the further development of extreme learning machine (ELM). First, the ill-conditioning of hidden layer output matrix reduces the stability of ELM. Second, the complexity of singular value decomposition (SVD) for computing Moore-Penrose generalized inverse limits the learning speed of ELM. For these two problems, this paper proposes the partial Lanczos ELM (PL-ELM) which employs the hybrid of partial Lanczos bidiagonalization and SVD to compute output weights. Experimental results indicate that, compared with ELM, PL-ELM not only effectively improves the stability and generalization performance but also raises the learning speed. (C) 2009 Elsevier B.V. All rights reserved.