Understanding and comparing scalable Gaussian process regression for big data

• Main Authors: Liu, Haitao, Cai, Jianfei, Ong, Yew-Soon, Wang, Yi
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Gaussian Process; Big Data
• Online Access: https://hdl.handle.net/10356/139619
• Institution: Nanyang Technological University

As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification and optimization. The cubic complexity of standard GP however leads to poor scalability, which poses challenges in the...