Gaussian Process Regression for Structured Data Sets

被引：14

作者：

Belyaev, Mikhail ^{[1
,2
,3
]}

Burnaev, Evgeny ^{[1
,2
,3
]}

Kapushev, Yermek ^{[1
,2
]}

机构：

[1] Inst Informat Transmiss Problems, Moscow 127994, Russia

[2] DATADVANCE Llc, Moscow 109028, Russia

[3] MIPT, PreMoLab, Dolgoprudnyi 141700, Russia

来源：

STATISTICAL LEARNING AND DATA SCIENCES | 2015年 / 9047卷

关键词：

Gaussian process; Structured data; Regularization;

D O I：

10.1007/978-3-319-17091-6_6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Approximation algorithms are widely used in many engineering problems. To obtain a data set for approximation a factorial design of experiments is often used. In such case the size of the data set can be very large. Therefore, one of the most popular algorithms for approximation - Gaussian Process regression - can hardly be applied due to its computational complexity. In this paper a new approach for a Gaussian Process regression in case of a factorial design of experiments is proposed. It allows to efficiently compute exact inference and handle large multidimensional and anisotropic data sets.

引用

页码：106 / 115

页数：10

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