Discovering Association with Copula Entropy
Machine Learning
2020-04-15 v2 Information Theory
math.IT
Quantitative Methods
Methodology
Machine Learning
Abstract
Discovering associations is of central importance in scientific practices. Currently, most researches consider only linear association measured by correlation coefficient, which has its theoretical limitations. In this paper, we propose a new method for discovering association with copula entropy -- a universal applicable association measure for not only linear cases, but nonlinear cases. The advantage of the method based on copula entropy over traditional method is demonstrated on the NHANES data by discovering more biomedical meaningful associations.
Keywords
Cite
@article{arxiv.1907.12268,
title = {Discovering Association with Copula Entropy},
author = {Jian Ma},
journal= {arXiv preprint arXiv:1907.12268},
year = {2020}
}
Comments
Minor revision. The code is available at https://github.com/majianthu/copent