An Information-Theoretic Measure of Dependency Among Variables in Large Datasets

Ali Mousavi; Richard G. Baraniuk

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arXiv · 1508.04073

An Information-Theoretic Measure of Dependency Among Variables in Large Datasets

Ali Mousavi·Richard G. Baraniuk

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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.

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Ali Mousavi, Richard G. Baraniuk. 2015-08-17. An Information-Theoretic Measure of Dependency Among Variables in Large Datasets. https://arxiv.org/abs/1508.04073

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