Abstract
Gaussian process (GP) is a Bayesian nonparametric regression model, showing good performance in various applications. Similar to other computational models, Gaussian process frequently encounters the matrix-inverse problem during its model-tuning procedure. The matrix inversion is generally of O(N3) operations where N is the matrix dimension. We proposed using the O(N2)-operation quasi-Newton BFGS method to approximate/replace the exact inverse of covariance matrix in the GP context. As inspired during a paper revision, in this paper we show that by using the random-scaling technique, the accuracy and effectiveness of such a BFGS matrix-inverse approximation could be further improved. These random-scaling BFGS techniques could be widely generalized to other machine-learning systems which rely on explicit matrix-inverse.
Original language | English |
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Pages | 452-457 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 1 Oct 2007 |
Event | IEEE 22nd International Symposium on Intelligent Control, 2007. ISIC 2007. - Guangzhou, China Duration: 1 Oct 2007 → … |
Conference
Conference | IEEE 22nd International Symposium on Intelligent Control, 2007. ISIC 2007. |
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Country/Territory | China |
City | Guangzhou |
Period | 1/10/07 → … |
Keywords
- gaussian process
- matrix inverse approximation
- quasi-Newton BFGS method
- random scaling