MATEC Web Conf.
Volume 370, 20222022 RAPDASA-RobMech-PRASA-CoSAAMI Conference - Digital Technology in Product Development - The 23rd Annual International RAPDASA Conference joined by RobMech, PRASA and CoSAAMI
|Number of page(s)||9|
|Published online||01 December 2022|
Lossy compression of observations for Gaussian process regression
Department of Electrical & Electronic Engineering, Stellenbosch University, South Africa
* Corresponding author: email@example.com
This paper proposes a novel approach of Gaussian process observation set compression based on a squared difference measure. It is used to discard observations to speed up Gaussian process prediction while retaining the information encoded in the full set of observations. Furthermore, this paper compares the regression performance of a compressed Gaussian process to its uncompressed version and to a randomly downsampled Gaussian process for a standard two-dimensional test function. The empirical results of this paper show that this is an effective algorithm for Gaussian process compression, speeding up prediction while maintaining predictive accuracy with regards to the predicted means.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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