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Local receptive fields based extreme learning machine
Huang G.-B.1; Bai Z.1; Kasun L.L.C.1; Vong C.M.2
2015
Source PublicationIEEE Computational Intelligence Magazine
ISSN1556603X
Volume10Issue:2Pages:18
Abstract

Extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feedforward neural networks (SLFNs), provides efficient unified learning solutions for the applications of feature learning, clustering, regression and classification. Different from the common understanding and tenet that hidden neurons of neural networks need to be iteratively adjusted during training stage, ELM theories show that hidden neurons are important but need not be iteratively tuned. In fact, all the parameters of hidden nodes can be independent of training samples and randomly generated according to any continuous probability distribution. And the obtained ELM networks satisfy universal approximation and classification capability. The fully connected ELM architecture has been extensively studied. However, ELM with local connections has not attracted much research attention yet. This paper studies the general architecture of locally connected ELM, showing that: 1) ELM theories are naturally valid for local connections, thus introducing local receptive fields to the input layer; 2) each hidden node in ELM can be a combination of several hidden nodes (a subnetwork), which is also consistent with ELM theories. ELM theories may shed a light on the research of different local receptive fields including true biological receptive fields of which the exact shapes and formula may be unknown to human beings. As a specific example of such general architectures, random convolutional nodes and a pooling structure are implemented in this paper. Experimental results on the NORB dataset, a benchmark for object recognition, show that compared with conventional deep learning solutions, the proposed local receptive fields based ELM (ELM-LRF) reduces the error rate from 6.5% to 2.7% and increases the learning speed up to 200 times. © 2015 IEEE.

DOI10.1109/MCI.2015.2405316
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000352902900002
The Source to ArticleScopus
Fulltext Access
Citation statistics
Cited Times [WOS]:172   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore
2.Department of Computer and Information Science, University of Macau, China
Recommended Citation
GB/T 7714
Huang G.-B.,Bai Z.,Kasun L.L.C.,et al. Local receptive fields based extreme learning machine[J]. IEEE Computational Intelligence Magazine,2015,10(2):18.
APA Huang G.-B.,Bai Z.,Kasun L.L.C.,&Vong C.M..(2015).Local receptive fields based extreme learning machine.IEEE Computational Intelligence Magazine,10(2),18.
MLA Huang G.-B.,et al."Local receptive fields based extreme learning machine".IEEE Computational Intelligence Magazine 10.2(2015):18.
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