Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning

Danil Kuzin, Le Yang, Olga Isupova, Lyudmila Mihaylova

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Gaussian process regression is a machine learning approach which has been shown its power for estimation of unknown functions. However, Gaussian processes suffer from high computational complexity, as in a basic form they scale cubically with the number of observations. Several approaches based on inducing points were proposed to handle this problem in a static context. These methods though face challenges with real-time tasks and when the data is received sequentially over time. In this paper, a novel online algorithm for training sparse Gaussian process models is presented. It treats the mean and hyperparameters of the Gaussian process as the state and parameters of the ensemble Kalman filter, respectively. The online evaluation of the parameters and the state is performed on new upcoming samples of data. This procedure iteratively improves the accuracy of parameter estimates. The ensemble Kalman filter reduces the computational complexity required to obtain predictions with Gaussian processes preserving the accuracy level of these predictions. The performance of the proposed method is demonstrated on the synthetic dataset and real large dataset of UK house prices.

Original languageEnglish
Title of host publication2018 21st International Conference on Information Fusion, FUSION 2018
PublisherIEEE
Pages39-46
Number of pages8
ISBN (Electronic)978-0-9964527-6-2
ISBN (Print)978-1-5386-4330-3
DOIs
Publication statusPublished - 6 Sep 2018
Event21st International Conference on Information Fusion, FUSION 2018 - Cambridge, UK United Kingdom
Duration: 10 Jul 201813 Jul 2018

Publication series

Name2018 21st International Conference on Information Fusion, FUSION 2018

Conference

Conference21st International Conference on Information Fusion, FUSION 2018
CountryUK United Kingdom
CityCambridge
Period10/07/1813/07/18

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Statistics, Probability and Uncertainty
  • Instrumentation

Cite this

Kuzin, D., Yang, L., Isupova, O., & Mihaylova, L. (2018). Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning. In 2018 21st International Conference on Information Fusion, FUSION 2018 (pp. 39-46). (2018 21st International Conference on Information Fusion, FUSION 2018). IEEE. https://doi.org/10.23919/ICIF.2018.8455785