TY - JOUR
T1 - Review of data structures for computationally efficient nearest-neighbour entropy estimators for large systems with periodic boundary conditions
AU - Brown, Joshua M.
AU - Bossomaier, Terry
AU - Barnett, Lionel
N1 - Includes bibliographical references.
PY - 2017/11
Y1 - 2017/11
N2 - 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.
AB - 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.
KW - Information theory
KW - Periodic boundary conditions
KW - Spatial partitioning
KW - Transfer entropy
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U2 - 10.1016/j.jocs.2017.10.019
DO - 10.1016/j.jocs.2017.10.019
M3 - Article
AN - SCOPUS:85032381322
SN - 1877-7503
VL - 23
SP - 109
EP - 117
JO - Journal of Computational Science
JF - Journal of Computational Science
ER -