Review of data structures for computationally efficient nearest-neighbour entropy estimators for large systems with periodic boundary conditions

被引：7

作者：

Brown, Joshua M. ^{[1
]}

Bossomaier, Terry ^{[2
]}

Barnett, Lionel ^{[3
]}

机构：

[1] Charles Sturt Univ, Sch Comp & Math, Panorama Ave, Bathurst, NSW 2795, Australia

[2] Charles Sturt Univ, Ctr Res Complex Syst, Panorama Ave, Bathurst, NSW 2795, Australia

[3] Univ Sussex, Dept Informat, Sackler Ctr Consciousness Sci, Brighton, E Sussex, England

来源：

JOURNAL OF COMPUTATIONAL SCIENCE | 2017年 / 23卷

基金：

澳大利亚研究理事会;

关键词：

Information theory; Transfer entropy; Periodic boundary conditions; Spatial partitioning; MUTUAL-INFORMATION; VORONOI DIAGRAMS; RANDOM POINTS; DISTANCE;

D O I：

10.1016/j.jocs.2017.10.019

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Information theoretic quantities are extremely useful in discovering relationships between two or more data sets. One popular method particularly for continuous systems for estimating these quantities is the nearest neighbour estimators. When system sizes are very large or the systems have periodic boundary conditions issues with performance and correctness surface, however solutions are known for each problem. Here we show that these solutions are inappropriate in systems that simultaneously contain both features and discuss a lesser known alternative solution involving Vantage Point trees that is capable of addressing both issues. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：109 / 117

页数：9

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