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• W2014530379 abstract "Gaussian process (GP) is a Bayesian nonparametric regression model, showing good performance in various applications. However, during its model-tuning procedure, the GP implementation suffers from numerous covariance-matrix inversions of expensive O(N3) operations, where N is the matrix dimension. In this article, we propose using the quasi-Newton BFGS O(N2)-operation formula to approximate/replace recursively the inverse of covariance matrix at every iteration. The implementation accuracy is guaranteed carefully by a matrix-trace criterion and by the restarts technique to generate good initial guesses. A number of numerical tests are then performed based on the sinusoidal regression example and the Wiener–Hammerstein identification example. It is shown that by using the proposed implementation, more than 80% O(N3) operations could be eliminated, and a typical speedup of 5–9 could be achieved as compared to the standard maximum-likelihood-estimation (MLE) implementation commonly used in Gaussian process regression." @default.
• W2014530379 created "2016-06-24" @default.
• W2014530379 creator A5053064477 @default.
• W2014530379 creator A5085704207 @default.
• W2014530379 date "2007-01-01" @default.
• W2014530379 modified "2023-10-12" @default.
• W2014530379 title "O(N2)-Operation Approximation of Covariance Matrix Inverse in Gaussian Process Regression Based on Quasi-Newton BFGS Method" @default.
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• W2014530379 doi "https://doi.org/10.1080/03610910601161298" @default.
• W2014530379 hasPublicationYear "2007" @default.
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