Application of offset estimator of differential entropy and mutual information with multivariate data
Application of offset estimator of differential entropy and mutual information with multivariate data
复制标题
微分熵和互信息偏移估计器在多元数据中的应用
DOI：
10.1017/exp.2022.14
复制
发表时间：
2022
影响因子：
--
通讯作者：
Marín-Franch I
中科院分区：
文献类型：
--
作者：
Marín-Franch I
Numerical estimators of differential entropy and mutual information can be slow to converge as sample size increases. The offset Kozachenko–Leonenko (KLo) method described here implements an offset version of the Kozachenko–Leonenko estimator that can markedly improve convergence. Its use is illustrated in applications to the comparison of trivariate data from successive scene color images and the comparison of univariate data from stereophonic music tracks. Publicly available code for KLo estimation of both differential entropy and mutual information is provided for R, Python, and MATLAB computing environments at https://github.com/imarinfr/klo.
影响因子：
4.5
作者：
Berrett, Thomas B.;Samworth, Richard J.;Yuan, Ming
通讯作者：
Yuan, Ming