Partial Lanczos extreme learning machine for single-output regression problems

Authors:
Xiaoliang Tang;Min Han
Affiliations:
School of Electronic and Information Engineering, Dalian University of Technology, 2 Linggong Lu, Ganjingzi Qu, Dalian 116023, China;School of Electronic and Information Engineering, Dalian University of Technology, 2 Linggong Lu, Ganjingzi Qu, Dalian 116023, China
Venue:
Neurocomputing
Year:
2009

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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.