An information-theoretic measure of dependency among variables in large datasets

Ali Mousavi; Richard G. Baraniuk

doi:10.1109/ALLERTON.2015.7447066

An information-theoretic measure of dependency among variables in large datasets

Ali Mousavi, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

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.

Original language	English (US)
Title of host publication	2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	650-657
Number of pages	8
ISBN (Electronic)	9781509018239
DOIs	https://doi.org/10.1109/ALLERTON.2015.7447066
State	Published - Apr 4 2016
Event	53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015 - Monticello, United States Duration: Sep 29 2015 → Oct 2 2015

Publication series

Name	2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015

Other

Other	53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
Country/Territory	United States
City	Monticello
Period	9/29/15 → 10/2/15

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Control and Systems Engineering

Access to Document

10.1109/ALLERTON.2015.7447066

Cite this

Mousavi, A., & Baraniuk, R. G. (2016). An information-theoretic measure of dependency among variables in large datasets. In 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015 (pp. 650-657). Article 7447066 (2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ALLERTON.2015.7447066

An information-theoretic measure of dependency among variables in large datasets. / Mousavi, Ali; Baraniuk, Richard G.
2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 650-657 7447066 (2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mousavi, A & Baraniuk, RG 2016, An information-theoretic measure of dependency among variables in large datasets. in 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015., 7447066, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015, Institute of Electrical and Electronics Engineers Inc., pp. 650-657, 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015, Monticello, United States, 9/29/15. https://doi.org/10.1109/ALLERTON.2015.7447066

Mousavi A, Baraniuk RG. An information-theoretic measure of dependency among variables in large datasets. In 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 650-657. 7447066. (2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015). doi: 10.1109/ALLERTON.2015.7447066

Mousavi, Ali ; Baraniuk, Richard G. / An information-theoretic measure of dependency among variables in large datasets. 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 650-657 (2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015).

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An information-theoretic measure of dependency among variables in large datasets

Abstract

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