Please use this identifier to cite or link to this item:
Title: When Gaussian process meets big data : a review of scalable GPs
Authors: Liu, Haitao
Ong, Yew-Soon
Shen, Xiaobo
Cai, Jianfei
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Liu, H., Ong, Y., Shen, X. & Cai, J. (2020). When Gaussian process meets big data : a review of scalable GPs. IEEE Transactions On Neural Networks and Learning Systems, 31(11), 4405-4423.
Journal: IEEE Transactions on Neural Networks and Learning Systems
Abstract: The vast quantity of information brought by big data as well as the evolving computer hardware encourages success stories in the machine learning community. In the meanwhile, it poses challenges for the Gaussian process regression (GPR), a well-known nonparametric, and interpretable Bayesian model, which suffers from cubic complexity to data size. To improve the scalability while retaining desirable prediction quality, a variety of scalable GPs have been presented. However, they have not yet been comprehensively reviewed and analyzed to be well understood by both academia and industry. The review of scalable GPs in the GP community is timely and important due to the explosion of data size. To this end, this article is devoted to reviewing state-of-The-Art scalable GPs involving two main categories: global approximations that distillate the entire data and local approximations that divide the data for subspace learning. Particularly, for global approximations, we mainly focus on sparse approximations comprising prior approximations that modify the prior but perform exact inference, posterior approximations that retain exact prior but perform approximate inference, and structured sparse approximations that exploit specific structures in kernel matrix; for local approximations, we highlight the mixture/product of experts that conducts model averaging from multiple local experts to boost predictions. To present a complete review, recent advances for improving the scalability and capability of scalable GPs are reviewed. Finally, the extensions and open issues of scalable GPs in various scenarios are reviewed and discussed to inspire novel ideas for future research avenues.
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2019.2957109
Schools: School of Computer Science and Engineering 
Research Centres: Rolls-Royce@NTU Corporate Lab 
Data Science and Artificial Intelligence Research Centre 
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

Files in This Item:
File Description SizeFormat 
When gaussian process meets big data.pdf2.7 MBAdobe PDFThumbnail

Citations 1

Updated on Sep 24, 2023

Web of ScienceTM
Citations 1

Updated on Sep 28, 2023

Page view(s)

Updated on Sep 29, 2023

Download(s) 5

Updated on Sep 29, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.