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).
Original language | English |
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Title of host publication | Proceedings of the 44th IEEE Conference on Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05 |
Subtitle of host publication | CDC-ECC '05 |
Publisher | IEEE |
Pages | 3711-3716 |
Number of pages | 6 |
ISBN (Print) | 0-7803-9567-0 |
DOIs | |
Publication status | Published - Dec 2005 |
Event | 44th IEEE Conference on Decision and Control and European Control Conference - Seville, Spain Duration: 12 Dec 2005 → 15 Dec 2005 |
Conference
Conference | 44th IEEE Conference on Decision and Control and European Control Conference |
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Abbreviated title | CDC-ECC |
Country/Territory | Spain |
City | Seville |
Period | 12/12/05 → 15/12/05 |
Keywords
- acceleration
- aerodynamics
- bayesian methods
- noise measurement
- predictive models
- velocity measurement