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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Abstract

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
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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