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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

Hongli ZHANG

Yantai University

Jinglei LIU

Yantai University

The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.

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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 -