Zhang, J, Li, Y, Xiao, W et al. (1 more author) (2020) Robust Extreme Learning Machine for Modeling with Unknown Noise. Journal of the Franklin Institute, 357 (14). pp. 9885-9908. ISSN 0016-0032
Abstract
Extreme learning machine (ELM) is an emerging machine learning technique for training single hidden layer feedforward networks (SLFNs). During the training phase, ELM model can be created by simultaneously minimizing the modeling errors and norm of the output weights. Usually, squared loss is widely utilized in the objective function of ELMs, which is theoretically optimal for the Gaussian error distribution. However, in practice, data collected from uncertain and heterogeneous environments trivially result in unknown noise, which may be very complex and cannot be described well using any single distribution. In order to tackle this issue, in this paper, a robust ELM (R-ELM) is proposed for improving the modeling capability and robustness with Gaussian and non-Gaussian noise. In R-ELM, a modified objective function is constructed to fit the noise using mixture of Gaussian (MoG) to approximate any continuous distribution. In addition, the corresponding solution for the new objective function is developed based on expectation maximization (EM) algorithm. Comprehensive experiments, both on selected benchmark datasets and real world applications, demonstrate that the proposed R-ELM has better robustness and generalization performance than state-of-the-art machine learning approaches.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier Ltd. All rights reserved. This is an author produced version of a paper published in Journal of the Franklin Institute. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Extreme learning machine; non-Gaussian noise; mixture of Gaussian; expectation maximization algorithm |
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) |
Funding Information: | Funder Grant number Royal Society IE161218 Royal Society RGS\R2\180237 |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Jul 2020 14:56 |
Last Modified: | 15 Jul 2021 00:39 |
Status: | Published |
Publisher: | Elsevier BV |
Identification Number: | 10.1016/j.jfranklin.2020.06.027 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163594 |