Detecting Unbiased Associations in Large Data Sets

Liu, Chuanlu; Wang, Shuliang; Yuan, Huang; Liu, Xiaojia

doi:10.1089/big.2021.0193

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…In 2020, equitability was characterized again in terms of interval estimates [ 8 ]. Since MIC may overestimate the correlated value, which leads to the misidentification of the relationship without noiseless, to detect unbiased associations, Liu proposed unbiased correlation measure weighted information coefficient mean (WICM) [ 18 ]. To quantify the dependence between two random vectors of possibly different dimensions, Mordant proposed two coefficients that are based on the Wasserstein distance between the actual distribution and a reference distribution with independent components, but they were not designed with the goal of equitability [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

Detecting Trivariate Associations in High-Dimensional Datasets

Liu

Wang

Yuan

et al. 2022

Sensors

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Detecting correlations in high-dimensional datasets plays an important role in data mining and knowledge discovery. While recent works achieve promising results, detecting multivariable correlations especially trivariate associations still remains a challenge. For example, maximal information coefficient (MIC) introduces generality and equitability to detect bivariate correlations but fails to detect multivariable correlation. To solve the problem mentioned above, we proposed quadratic optimized trivariate information coefficient (QOTIC). Specifically, QOTIC equitably measures dependence among three variables. Our contributions are three-fold: (1) we present a novel quadratic optimization procedure to approach the correlation with high accuracy; (2) QOTIC exceeds existing methods in generality and equitability as QOTIC has general test functions and is applicable in detecting multivariable correlation in datasets of various sample sizes and noise levels; (3) QOTIC achieved both higher accuracy and higher time-efficiency than previous methods. Extensive experiments demonstrate the excellent performance of QOTIC.

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Section: Related Workmentioning

confidence: 99%