An Information-Theoretic Measure of Dependency Among Variables in Large Datasets
Information Theory
2015-08-18 v1 math.IT
Methodology
Abstract
The maximal information coefficient (MIC), which measures the amount of dependence between two variables, is able to detect both linear and non-linear associations. However, computational cost grows rapidly as a function of the dataset size. In this paper, we develop a computationally efficient approximation to the MIC that replaces its dynamic programming step with a much simpler technique based on the uniform partitioning of data grid. A variety of experiments demonstrate the quality of our approximation.
Cite
@article{arxiv.1508.04073,
title = {An Information-Theoretic Measure of Dependency Among Variables in Large Datasets},
author = {Ali Mousavi and Richard G. Baraniuk},
journal= {arXiv preprint arXiv:1508.04073},
year = {2015}
}