Improved Approximation Algorithm for Maximal Information Coefficient

Shuliang Wang, Yiping Zhao, Yue Shu and Wenzhong Shi
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Shuliang Wang: School of Software, Beijing Institute of Technology, Beijing, China
Yiping Zhao: Software Center, Bank of China, Beijing, China
Yue Shu: Tencent Technology (Beijing) Company Limited, Beijing, China
Wenzhong Shi: Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China

International Journal of Data Warehousing and Mining (IJDWM), 2017, vol. 13, issue 1, 76-93

Abstract: A novel statistical maximal information coefficient (MIC) that can detect the nonlinear relationships in large data sets was proposed by Reshef et al. (2011), with emphasis being placed on the equitability, which is a very important concept in data exploration. In this paper, an improved algorithm for approximation of the MIC (IAMIC) is proposed for the development of the equitability. Based on quadratic optimization processes, the IAMIC can search for a more optimal partition on the y-axis rather than use that which was obtained simply through the equipartition of the y-axis, to enable it to come closer to the true value of the MIC. It has been proved that the IAMIC can search for a local optimal value while using a lower number of iterations. It has also been shown that the IAMIC provides higher accuracy and a more acceptable run-time, based on both a mathematical proof and the results of simulations.

Date: 2017
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