Zhang, J, Li, Y, Xiao, W et al. (1 more author) (2020) Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines. Journal of the Franklin Institute, 357 (13). pp. 8925-8955. ISSN 0016-0032
Abstract
In the past decade, deep learning techniques have powered many aspects of our daily life, and drawn ever-increasing research interests. However, conventional deep learning approaches, such as deep belief network (DBN), restricted Boltzmann machine (RBM), and convolutional neural network (CNN), suffer from time-consuming training process due to fine-tuning of a large number of parameters and the complicated hierarchical structure. Furthermore, the above complication makes it difficult to theoretically analyze and prove the universal approximation of those conventional deep learning approaches. In order to tackle the issues, multilayer extreme learning machines (ML-ELM) were proposed, which accelerate the development of deep learning. Compared with conventional deep learning, ML-ELMs are non-iterative and fast due to the random feature mapping mechanism. In this paper, we perform a thorough review on the development of ML-ELMs, including stacked ELM autoencoder (ELM-AE), residual ELM, and local receptive field based ELM (ELM-LRF), as well as address their applications. In addition, we also discuss the connection between random neural networks and conventional deep learning.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved. This is an author produced version of an article published in Journal of the Franklin Institute. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Sep 2020 09:46 |
Last Modified: | 08 Jul 2021 00:39 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jfranklin.2020.04.033 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:165049 |