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International Federation of Automatic Control (IFAC), IFAC-PapersOnLine, 28(48), p. 703-708, 2015

DOI: 10.1016/j.ifacol.2015.12.212

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Online sparse Gaussian process regression using FITC and PITC approximations**This research is supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organisation for Scientific Research (NWO), and which is partly funded by the Ministry of Economic Affairs. The work was also supported by the Swedish research Council (VR) via the project Probabilistic modeling of dynamical systems (Contract number: 621-2013-5524).

Journal article published in 2015 by Hildo Bijl, Jan-Willem van Wingerden, Thomas B. Schön ORCID, Michel Verhaegen
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

We provide a method which allows for online updating of sparse Gaussian Process (GP) regression algorithms for any set of inducing inputs. This method is derived both for the Fully Independent Training Conditional (FITC) and the Partially Independent Training Conditional (PITC) approximation, and it allows the inclusion of a new measurement point xn+1 in O(m2) time, with m denoting the size of the set of inducing inputs. Due to the online nature of the algorithms, it is possible to forget earlier measurement data, which means that also the memory space required is O(m2), both for FITC and PITC. We show that this method is able to efficiently apply GP regression to a large data set with accurate results.