Kuzin, D., Yang, L., Isupova, O. et al. (1 more author) (2018) Ensemble Kalman filtering for online Gaussian process regression and learning. In: 2018 21st International Conference on Information Fusion (FUSION). 2018 21st International Conference on Information Fusion (FUSION), 10-13 Jul 2018, Cambridge, UK. IEEE , UK , pp. 39-46. ISBN 978-0-9964527-6-2
Abstract
Gaussian process regression is a machine learning approach which has been shown its power for estimation of unknown functions. However, Gaussian processes suffer from high computational complexity, as in a basic form they scale cubically with the number of observations. Several approaches based on inducing points were proposed to handle this problem in a static context. These methods though face challenges with real-time tasks and when the data is received sequentially over time. In this paper, a novel online algorithm for training sparse Gaussian process models is presented. It treats the mean and hyperparameters of the Gaussian process as the state and parameters of the ensemble Kalman filter, respectively. The online evaluation of the parameters and the state is performed on new upcoming samples of data. This procedure iteratively improves the accuracy of parameter estimates. The ensemble Kalman filter reduces the computational complexity required to obtain predictions with Gaussian processes preserving the accuracy level of these predictions. The performance of the proposed method is demonstrated on the synthetic dataset and real large dataset of UK house prices.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 ISIF. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Kalman filters; Gaussian processes; Estimation; Prediction algorithms; Bayes methods; Covariance matrices; Noise measurement |
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) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Sep 2018 15:21 |
Last Modified: | 24 Sep 2018 15:21 |
Published Version: | https://doi.org/10.23919/ICIF.2018.8455785 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/ICIF.2018.8455785 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:136088 |