Random scaling of quasi-Newton BFGS method to improve the o(N^2)-operation approximation of covariance-matrix inverse in Gaussian process

Y. Zhang, W.E. Leithead, D. Leith

Research output: Contribution to conferencePaper

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 languageEnglish
Pages452-457
Number of pages6
DOIs
Publication statusPublished - 1 Oct 2007
EventIEEE 22nd International Symposium on Intelligent Control, 2007. ISIC 2007. - Guangzhou, China
Duration: 1 Oct 2007 → …

Conference

ConferenceIEEE 22nd International Symposium on Intelligent Control, 2007. ISIC 2007.
CountryChina
CityGuangzhou
Period1/10/07 → …

Keywords

  • gaussian process
  • matrix inverse approximation
  • quasi-Newton BFGS method
  • random scaling

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