A method for continuous-range sequence analysis with Jensen-Shannon divergence

IF 1.2 Q3 PHYSICS, MULTIDISCIPLINARY

Papers in Physics Pub Date : 2021-02-05 DOI:10.4279/PIP.130001

M. Ré, G. G. A. Varela

引用次数: 0

Abstract

Mutual Information (MI) is a useful Information Theory tool for the recognition of mutual dependence between data sets. Several methods have been developed fore estimation of MI when both data sets are of the discrete type or when both are of the continuous type. However, MI estimation between a discrete range data set and a continuous range data set has not received so much attention. We therefore present here a method for the estimation of MI for this case, based on the kernel density approximation. This calculation may be of interest in diverse contexts. Since MI is closely related to the Jensen Shannon divergence, the method developed here is of particular interest in the problems of sequence segmentation and set comparisons.

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基于Jensen-Shannon散度的连续范围序列分析方法

互信息(MI)是一种有用的信息理论工具，用于识别数据集之间的相互依赖性。当两个数据集都是离散类型或两个数据集都是连续类型时，已经开发了几种估计MI的方法。然而，离散距离数据集和连续距离数据集之间的MI估计并没有受到如此多的关注。因此，我们在这里提出了一种基于核密度近似的估计这种情况下MI的方法。这种计算在不同的背景下可能会引起人们的兴趣。由于MI与Jensen Shannon散度密切相关，因此这里开发的方法对序列分割和集合比较问题特别感兴趣。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Papers in Physics PHYSICS, MULTIDISCIPLINARY-

CiteScore

1.90

自引率

0.00%

发文量

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