Reference : Sparse Gaussian Process Based On Hat Basis Functions
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/44376
Sparse Gaussian Process Based On Hat Basis Functions
English
Fang, W. [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, P.R. China]
Li, H. [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, P.R. China]
Huang, Hui mailto [University of Luxembourg > > >]
Dang, S. [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, P.R. China]
Huang, Z. [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, P.R. China]
Wang, Z. [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, P.R. China]
28-Aug-2020
2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)
IEEE
1-5
Yes
No
International
978-1-7281-7117-3
2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)
12-13 June 2020
IEEE
Istanbul
Turkey
[en] Gaussian processes;Kernel;Training data;Covariance matrices;Training;Mathematical model;Computational complexity;Gaussian process;hat basis function;sparse Gaussian process;spectral approximation
[en] Gaussian process is a popular non-parametric Bayesian methodology for modeling the regression problem, which is completely determined by its mean and covariance function. Nevertheless, this method still has two major disadvantages: it is difficult to handle large datasets and may not meet inequality constraints in specific problems. These two issues have been addressed by the so-called sparse Gaussian process and constrained Gaussian process in recent years. In this paper, to reduce the overall computational complexity in the exact Gaussian process, we propose a new sparse Gaussian process method to solve the unconstrained regression problem. The idea is inspired by the constrained Gaussian process method. The critical point of our method is that we introduce the hat basis function, which is mentioned in the constrained Gaussian process, and modify its definition according to the range of training or test data. It turns out that this method belongs to the spectral approximation methods. Similar to the exact Gaussian process and Gaussian process with Fully Independent Training Conditional approximation, our method obtains satisfactory approximate results on analytical functions or open-source datasets.
http://hdl.handle.net/10993/44376
10.1109/ICECCE49384.2020.9179226

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