The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
과학 및 산업 분야에서 대량의 데이터가 출현함에 따라 예측 정확도를 향상하고 GPR(가우스 과정 회귀)의 높은 복잡성을 줄이는 것이 시급합니다. 그러나 전통적인 전역 근사와 국소 근사에는 상응하는 단점이 있습니다. 전역 근사는 국소 특징을 무시하는 경향이 있고 국소 근사는 과적합 문제가 있습니다. 이러한 문제를 해결하기 위해 무작위 푸리에 특징(RFF)과 국소 근사를 결합한 대규모 가우시안 프로세스 회귀 알고리즘(RFFLT)이 제안되었습니다. 1) 훈련 시간을 단축하기 위해 무작위 저차원 특징 공간에 매핑된 무작위 푸리에 특징 맵 입력 데이터를 사용하여 처리합니다. 알고리즘의 주요 혁신은 기존의 빠른 선형 처리 방법을 사용하여 특징을 설계하여 변환된 데이터의 내적이 사용자가 지정한 이동 불변 커널의 특징 공간 내 내적과 거의 동일하도록 하는 것입니다. 2) Tsallis 상호 정보 방법을 기반으로 하는 일반화된 견고한 베이지안 위원회 머신(GRBCM)은 로컬 근사에 사용되며, 이는 모델의 유연성을 향상시키고 이전 작업과 비교하여 전문가 가중치 분포의 희박한 표현을 생성합니다. 알고리즘 RFFLT는 XNUMX개의 실제 데이터 세트에서 테스트되었으며, 이는 회귀 예측 시간을 크게 단축하고 예측 정확도를 향상시켰습니다.
Hongli ZHANG
Yantai University
Jinglei LIU
Yantai University
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부
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
부
@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},}
부
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 -