MIC theory proof with its application

DOI

10.2991/978-94-6463-540-9_38How to use a DOI?

Keywords

Mutual information; Entropy; Grid-partition; Correlation coefficient; Maximal Information Coefficient

Abstract

Measuring dependencies between two variables in an extremely large data set is an increasingly important problem, naturally then the methods to solve such problems warrants equal if not greater attention. This paper aims to overview an effective measure of dependence, the MIC. This statistical measure is equitable giving no preference to certain function types. It is also general, being able to analyze both linear and nonlinear function types as well as combinations and superpositions of both. The key methodology such as the definitions and steps of MIC are explained as well as a proof of the central recursive algorithm which allows realistic runtimes for MIC. Other heuristic and approximations that make it both an accurate and efficient algorithm are also covered, namely the purpose and effect of equipartition and the clumping of the master partition. MICe, an approximation of MIC is also explained. This approximation fully utilizes the two heuristics of equipartition and clumping. This paper also briefly explains why these simplifications can still provide accurate results with a significantly faster runtime.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Dongsheng Wang
PY  - 2024
DA  - 2024/10/16
TI  - MIC theory proof with its application
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 380
EP  - 389
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_38
DO  - 10.2991/978-94-6463-540-9_38
ID  - Wang2024
ER  -