Large-Scale Gaussian Process Regression Based on Random Fourier Features and Local Approximation with Tsallis Entropy

Hongli ZHANG; Jinglei LIU

doi:10.1587/transinf.2023EDL8016

Large-Scale Gaussian Process Regression Based on Random Fourier Features and Local Approximation with Tsallis Entropy

Hongli ZHANG, Jinglei LIU

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With the emergence of a large quantity of data in science and industry, it is urgent to improve the prediction accuracy and reduce the high complexity of Gaussian process regression (GPR). However, the traditional global approximation and local approximation have corresponding shortcomings, such as global approximation tends to ignore local features, and local approximation has the problem of over-fitting. In order to solve these problems, a large-scale Gaussian process regression algorithm (RFFLT) combining random Fourier features (RFF) and local approximation is proposed. 1) In order to speed up the training time, we use the random Fourier feature map input data mapped to the random low-dimensional feature space for processing. The main innovation of the algorithm is to design features by using existing fast linear processing methods, so that the inner product of the transformed data is approximately equal to the inner product in the feature space of the shift invariant kernel specified by the user. 2) The generalized robust Bayesian committee machine (GRBCM) based on Tsallis mutual information method is used in local approximation, which enhances the flexibility of the model and generates a sparse representation of the expert weight distribution compared with previous work. The algorithm RFFLT was tested on six real data sets, which greatly shortened the time of regression prediction and improved the prediction accuracy.

Publication: IEICE TRANSACTIONS on Information Vol.E106-D No.10 pp.1747-1751

Publication Date: 2023/10/01

Publicized: 2023/07/11

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2023EDL8016

Type of Manuscript: LETTER

Category: Artificial Intelligence, Data Mining

Authors

Hongli ZHANG
Yantai University
Jinglei LIU
Yantai University

Keyword

Gaussian process regression, random Fourier features, local approximation, Tsallis entropy

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Hongli ZHANG, Jinglei LIU, "Large-Scale Gaussian Process Regression Based on Random Fourier Features and Local Approximation with Tsallis Entropy" in IEICE TRANSACTIONS on Information, vol. E106-D, no. 10, pp. 1747-1751, October 2023, doi: 10.1587/transinf.2023EDL8016.
Abstract: With the emergence of a large quantity of data in science and industry, it is urgent to improve the prediction accuracy and reduce the high complexity of Gaussian process regression (GPR). However, the traditional global approximation and local approximation have corresponding shortcomings, such as global approximation tends to ignore local features, and local approximation has the problem of over-fitting. In order to solve these problems, a large-scale Gaussian process regression algorithm (RFFLT) combining random Fourier features (RFF) and local approximation is proposed. 1) In order to speed up the training time, we use the random Fourier feature map input data mapped to the random low-dimensional feature space for processing. The main innovation of the algorithm is to design features by using existing fast linear processing methods, so that the inner product of the transformed data is approximately equal to the inner product in the feature space of the shift invariant kernel specified by the user. 2) The generalized robust Bayesian committee machine (GRBCM) based on Tsallis mutual information method is used in local approximation, which enhances the flexibility of the model and generates a sparse representation of the expert weight distribution compared with previous work. The algorithm RFFLT was tested on six real data sets, which greatly shortened the time of regression prediction and improved the prediction accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8016/_p

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@ARTICLE{e106-d_10_1747,
author={Hongli ZHANG, Jinglei LIU, },
journal={IEICE TRANSACTIONS on Information},
title={Large-Scale Gaussian Process Regression Based on Random Fourier Features and Local Approximation with Tsallis Entropy},
year={2023},
volume={E106-D},
number={10},
pages={1747-1751},
abstract={With the emergence of a large quantity of data in science and industry, it is urgent to improve the prediction accuracy and reduce the high complexity of Gaussian process regression (GPR). However, the traditional global approximation and local approximation have corresponding shortcomings, such as global approximation tends to ignore local features, and local approximation has the problem of over-fitting. In order to solve these problems, a large-scale Gaussian process regression algorithm (RFFLT) combining random Fourier features (RFF) and local approximation is proposed. 1) In order to speed up the training time, we use the random Fourier feature map input data mapped to the random low-dimensional feature space for processing. The main innovation of the algorithm is to design features by using existing fast linear processing methods, so that the inner product of the transformed data is approximately equal to the inner product in the feature space of the shift invariant kernel specified by the user. 2) The generalized robust Bayesian committee machine (GRBCM) based on Tsallis mutual information method is used in local approximation, which enhances the flexibility of the model and generates a sparse representation of the expert weight distribution compared with previous work. The algorithm RFFLT was tested on six real data sets, which greatly shortened the time of regression prediction and improved the prediction accuracy.},
keywords={},
doi={10.1587/transinf.2023EDL8016},
ISSN={1745-1361},
month={October},}

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TY - JOUR
TI - Large-Scale Gaussian Process Regression Based on Random Fourier Features and Local Approximation with Tsallis Entropy
T2 - IEICE TRANSACTIONS on Information
SP - 1747
EP - 1751
AU - Hongli ZHANG
AU - Jinglei LIU
PY - 2023
DO - 10.1587/transinf.2023EDL8016
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2023
AB - With the emergence of a large quantity of data in science and industry, it is urgent to improve the prediction accuracy and reduce the high complexity of Gaussian process regression (GPR). However, the traditional global approximation and local approximation have corresponding shortcomings, such as global approximation tends to ignore local features, and local approximation has the problem of over-fitting. In order to solve these problems, a large-scale Gaussian process regression algorithm (RFFLT) combining random Fourier features (RFF) and local approximation is proposed. 1) In order to speed up the training time, we use the random Fourier feature map input data mapped to the random low-dimensional feature space for processing. The main innovation of the algorithm is to design features by using existing fast linear processing methods, so that the inner product of the transformed data is approximately equal to the inner product in the feature space of the shift invariant kernel specified by the user. 2) The generalized robust Bayesian committee machine (GRBCM) based on Tsallis mutual information method is used in local approximation, which enhances the flexibility of the model and generates a sparse representation of the expert weight distribution compared with previous work. The algorithm RFFLT was tested on six real data sets, which greatly shortened the time of regression prediction and improved the prediction accuracy.
ER -