微分熵和互信息偏置估计在多元数据中的应用

I. Marín-Franch, Martín Sanz-Sabater, David H. Foster
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引用次数: 1

摘要

随着样本大小的增加,微分熵和互信息的数值估计收敛速度较慢。这里描述的偏移Kozachenko-Leonenko (KLo)方法实现了Kozachenko-Leonenko估计器的偏移版本,可以显著提高收敛性。它的用途在应用中说明了比较从连续的场景彩色图像的三变量数据和比较立体声音乐轨道的单变量数据。在https://github.com/imarinfr/klo上提供了用于R、Python和MATLAB计算环境的微分熵和互信息的KLo估计的公开可用代码。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of offset estimator of differential entropy and mutual information with multivariate data
Abstract 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.
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