Time-series Gaussian process regression based on toeplitz computation of O(N2) operations and O(N)-level storage
Zhang, Y. and Leithead, W.E. and Leith, D.J.; (2005) Time-series Gaussian process regression based on toeplitz computation of O(N2) operations and O(N)-level storage. In: Proceedings of the 44th IEEE Conference on Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05. IEEE, ESP, pp. 3711-3716. ISBN 0-7803-9567-0
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Gaussian process (GP) regression is a Bayesian nonparametric model showing good performance in various applications. However, its hyperparameter-estimating procedure may contain numerous matrix manipulations of O(N3) arithmetic operations, in addition to the O(N2)-level storage. Motivated by handling the real-world large dataset of 24000 wind-turbine data, we propose in this paper an efficient and economical Toeplitz-computation scheme for time-series Gaussian process regression. The scheme is of O(N2) operations and O(N)-level memory requirement. Numerical experiments substantiate the effectiveness and possibility of using this Toeplitz computation for very large datasets regression (such as, containing 10000~100000 data points).
Creators(s): | Zhang, Y., Leithead, W.E. and Leith, D.J.; | Item type: | Book Section |
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ID code: | 38120 |
Keywords: | acceleration, aerodynamics , bayesian methods, noise measurement, predictive models, velocity measurement, Electrical engineering. Electronics Nuclear engineering |
Subjects: | Technology > Electrical engineering. Electronics Nuclear engineering |
Department: | Faculty of Engineering > Electronic and Electrical Engineering |
Depositing user: | Pure Administrator |
Date deposited: | 02 Mar 2012 14:37 |
Last modified: | 01 Jan 2021 13:59 |
URI: | https://strathprints.strath.ac.uk/id/eprint/38120 |
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