Wang, P., Mihaylova, L. orcid.org/0000-0001-5856-2223, Munir, S. et al. (6 more authors) (2021) A computationally efficient symmetric diagonally dominant matrix projection-based Gaussian process approach. Signal Processing, 183. 108034. ISSN 0165-1684
Abstract
Although kernel approximation methods have been widely applied to mitigate the O(n3) cost of the n × n kernel matrix inverse in Gaussian process methods, they still face computational challenges. The ‘residual’ matrix between the covariance and the approximating component is often discarded as it prevents the computational cost reduction. In this paper, we propose a computationally efficient Gaussian process approach that achieves better computational efficiency, O(mn2), compared with standard Gaussian process methods, when using m n data. The proposed approach incorporates the ‘residual’ matrix in its symmetric diagonally dominant form which can be further approximated by the Neumann series. We have validated and compared the approach with full Gaussian process approaches and kernel approximation based Gaussian process variants, both on synthetic and real air quality data.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Published by Elsevier B.V. This is an author produced version of a paper subsequently published in Signal Processing. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Gaussian process methods; Symmetric diagonally dominant projection; Kernel approximation; Sustainable development; Air quality forecasting |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Civil and Structural Engineering (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/T013265/1 Engineering and Physical Sciences Research Council EP/T013265/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Feb 2021 10:25 |
Last Modified: | 17 Feb 2022 10:27 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.sigpro.2021.108034 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:171099 |
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