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

Ali Mousavi, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 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 languageEnglish (US)
Title of host publication2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages650-657
Number of pages8
ISBN (Electronic)9781509018239
DOIs
StatePublished - Apr 4 2016
Event53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015 - Monticello, United States
Duration: Sep 29 2015Oct 2 2015

Publication series

Name2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015

Other

Other53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
CountryUnited States
CityMonticello
Period9/29/1510/2/15

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Control and Systems Engineering

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