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An Extension of MIC for Multivariate Correlation Analysis Based on Interaction Information

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You-Wei XuAuthors Info & Claims

CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

Pages 399 - 403

https://doi.org/10.1145/3297156.3297162

Published: 08 December 2018 Publication History

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Abstract

Maximal Information Coefficient (MIC) is a novel bivariate correlation analysis method that attracts a lot of attention from various research fields, due to its generality and equitability. So on the basis of interaction information, we propose to extend MIC into the context of multivariate correlation analysis. From the results of simulative experiments, our propose method can detect a wide range of associations by assigning 1 to noiseless functional data sets and 0 to statistically independent data sets, verifying its generality, equitability and robustness.

References

[1]

Reshef, D. N., Reshef, Y. A., Finucane, H. K., Grossman, S. R., Mcvean, G., & Turnbaugh, P. J., et al. (2011). Detecting novel associations in large data sets. Science, 334(6062), 1518.

Crossref

Google Scholar

[2]

Reshef, D., Reshef, Y., Mitzenmacher, M., & Sabeti, P. (2013). Equitability analysis of the maximal information coefficient, with comparisons. Computer Science.

Google Scholar

[3]

Jiang, Y. S., Zhang, Q. L., & Liu, C. (2013). Some novel measurement on exploring large data sets based on multi-variables mutual information theory. Journal of Theoretical & Applied Information Technology, 47(2), 547--550.

Google Scholar

[4]

Jiang, Y., & Zhang, Q. (2015). On Improved 3MIC Algorithm on Exploring Large Data Sets with Multi-variables and Application. International Conference on Intelligent Human-Machine Systems and Cybernetics (pp.157--160). IEEE Computer Society.

Digital Library

Google Scholar

[5]

Shao, F., Li, K., & Dong, Y. (2017). Identifying multi-variable relationships based on the maximal information coefficient. Intelligent Data Analysis, 21(1), 151--166.

Crossref

Google Scholar

[6]

C. E. Shannon. 2001. A mathematical theory of communication. SIGMOBILE Mob. Comput. Commun. Rev. 5, 1 (January 2001), 3--55.

Digital Library

Google Scholar

[7]

Timme, N., Alford, W., Flecker, B., & Beggs, J. M. (2012). Multivariate information measures: an experimentalist's perspective. Journal of Computational Neuroscience, 36(2), 119--140.

Digital Library

Google Scholar

[8]

Jakulin, A., & Bratko, I. (2003). Quantifying and visualizing attribute interactions. Computer Science, 308002, 3.

Google Scholar

[9]

Watanabe, S. (1960). Information theoretical analysis of multivariate correlation. Ibm Journal of Research & Development, 4(1), 66--82.

Digital Library

Google Scholar

[10]

Chechik, G., Globerson, A., Tishby, N., Anderson, M. J., Young, E. D., & Nelken, I. (2002). Group redundancy measures reveal redundancy reduction in the auditory pathway. Advances in Neural Information Processing Systems, 173--180.

Digital Library

Google Scholar

[11]

Williams, P. L., & Beer, R. D. (2010). Nonnegative decomposition of multivariate information. Physics, 1004.

Google Scholar

[12]

Yeung, R. W. (1991). A new outlook on shannon's information measures. IEEE Transactions on Information Theory, 37(3), 466--474.

Crossref

Google Scholar

[13]

Zhang, Y. H., Li, Y. J., & Zhang, T. (2015). Detecting multivariable correlation with maximal information entropy. Journal of Electronics & Information Technology.

Google Scholar

[14]

Wang, Q., Shen, Y., & Zhang, J. Q. (2005). A nonlinear correlation measure for multivariable data set. Physica D Nonlinear Phenomena, 200(3), 287--295.

Crossref

Google Scholar

Cited By

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Kamiura MSekine R(2023)Jaccard matrix for nonlinear filter statisticsSICE Journal of Control, Measurement, and System Integration10.1080/18824889.2023.219416916:1(152-163)Online publication date: 15-Apr-2023
https://doi.org/10.1080/18824889.2023.2194169
Sun ZWang LZhou JWang CYao XGan FDong MTian J(2023)Prediction of the inertial permeability of a 2D single rough fracture based on geometric informationActa Geotechnica10.1007/s11440-023-02039-419:4(2105-2124)Online publication date: 1-Sep-2023
https://doi.org/10.1007/s11440-023-02039-4

Index Terms

An Extension of MIC for Multivariate Correlation Analysis Based on Interaction Information
1. Information systems
  1. Information systems applications
    1. Data mining

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CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

December 2018

641 pages

ISBN:9781450366069

DOI:10.1145/3297156

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 December 2018

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CSAI '18: 2018 2nd International Conference on Computer Science and Artificial Intelligence

December 8 - 10, 2018

Shenzhen, China

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Kamiura MSekine R(2023)Jaccard matrix for nonlinear filter statisticsSICE Journal of Control, Measurement, and System Integration10.1080/18824889.2023.219416916:1(152-163)Online publication date: 15-Apr-2023
https://doi.org/10.1080/18824889.2023.2194169
Sun ZWang LZhou JWang CYao XGan FDong MTian J(2023)Prediction of the inertial permeability of a 2D single rough fracture based on geometric informationActa Geotechnica10.1007/s11440-023-02039-419:4(2105-2124)Online publication date: 1-Sep-2023
https://doi.org/10.1007/s11440-023-02039-4

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Using Spearman's correlation coefficients for exploratory data analysis on big dataset

Canonical correlation analysis based on information theory

A Unifying Discussion of Correlation Analysis for Complex Random Vectors

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