Evaluation of Statistical Relationship of Random Variables via Mutual Information

被引：0
|
作者：
Tsurko, V. V. [1 ]
Mikhalskii, A., I [1 ]
机构：
[1] Russian Acad Sci, Trapeznikov Inst Control Sci, Moscow 117997, Russia
来源：
AUTOMATION AND REMOTE CONTROL | 2022年 / 83卷 / 05期
关键词：
correlation coefficient; nonparametric evaluation of mutual information; reproducing-kernel Hilbert space; pentapeptide stability prediction;
D O I：
10.1134/S000511792205006X
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We consider the use of nonparametric evaluation of mutual information to determine the relationship between random variables. It is shown that in the presence of a nonlinear relationship between random variables, the correlation coefficient can give an incorrect result. A method is proposed for constructing an evaluation of mutual information from empirical data in an abstract reproducing-kernel Hilbert space. Using the generalized representer theorem, a method for nonparametric evaluation of mutual information is proposed. The operability of the method is demonstrated using the examples of the analysis of artificial data. The application of the method in predicting the stability of pentapeptides is described.
引用
收藏
页码：734 / 742
页数：9
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