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- W86543131 abstract "Gaussian process (GP) is a Bayesian nonparametric regression model, showing good performance in various applications. However, its hyperparameter-estimation procedure suffers from numerous covariance-matrix inversions of prohibitively O(N3) operations. In this paper, we propose using the quasi-Newton BFGS O(N2)-operation formula to update recursively the inverse of covariance matrix at every iteration. As for the involved log det computation, a power-series expansion based approximation and compensation scheme is proposed with only 50 N2 operations. A number of numerical tests are performed based on the 2D-sinusoidal regression example and the Wiener-Hammerstein identification example. It is shown that by using the proposed implementation, more than 80% O(N3) operations are eliminated, and the speedup of 5 ~9 can be achieved." @default.
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- W86543131 date "2005-01-01" @default.
- W86543131 modified "2023-10-08" @default.
- W86543131 title "EFFICIENT GAUSSIAN PROCESS BASED ON BFGS UPDATING AND LOGDET APPROXIMATION" @default.
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- W86543131 doi "https://doi.org/10.3182/20050703-6-cz-1902.00218" @default.
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