An online Bayesian filtering framework for Gaussian process regression: Application to global surface temperature analysis |
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| Affiliation: | 1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China;2. Department of Computer Science and Software Engineering, Laval University, Canada;1. Department of Computer Science and Engineering, University of Dhaka, Bangladesh;2. ICube Laboratory, University of Strasbourg, France;2. Telematics Engineering Department, University of Cauca, Sector Tulcán, Popayán, Colombia;3. System Engineering Department, University of Cauca, Sector Tulcán, Popayán, Colombia;4. Institute of Informatics, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil;5. Intelligent Management Systems Group, Foundation University of Popayán, Colombian;1. School of Electrical and Electronic Engineering, Biometrics Engineering Research Center (BERC), Yonsei University, B619, 2nd Engineering Hall, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea;2. Digital Media & Communications Business, Samsung Electronics Co.,Ltd, Digital Media & Communications R&D Center, Maetan 3-dong, Yeongtong-gu, Suwon-si, Gyeonggi-do, 443-742, Republic of Korea;1. School of Science & Technology, International Hellenic University, Thessaloniki, Greece;2. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece;3. Information Technologies Institute, Centre of Research & Technology Hellas, Thessaloniki, Greece;4. Ubitech Ltd., Athens, Greece;1. Tridimensional Technology Division, Center for Information Technology Renato Archer, Campinas-SP 13069-901, Brazil;2. Institute of Computing, University of Campinas, Campinas-SP 13083-852, Brazil |
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| Abstract: | Over the past centuries, global warming has gradually become one of the most significant issues in our life. Hence, it is crucial to analyze global surface temperature with an efficient and accurate model. Gaussian process (GP) is a popular nonparametric model, due to the power of Bayesian inference framework. However, the performance of GP is often deteriorated for large-scale data sets such as global surface temperature. In this work, we propose a novel online Bayesian filtering framework for large-scale GP regression. There are three contributions. Firstly, we develop a novel GP-based state space model to efficiently process data in a sequential manner. Secondly, based on our state space model, we design a marginalized particle filter to infer the latent function values and learn the model parameters online. It can efficiently reduce the computation burden of GP while improving the estimation accuracy in a recursive Bayesian inference framework. Finally, we successfully apply our approach to a number of synthetic data sets and the large-scale global surface temperature data set. The results show that our approach outperforms related GP variants, and it is an efficient and accurate expert system for global surface temperature analysis. |
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