Detection of Conditional Dependence Between Multiple Variables Using Multiinformation

Mielniczuk, Jan; Teisseyre, Paweł

doi:10.1007/978-3-030-77980-1_51

Cited by 1 publication

(1 citation statement)

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“…The extension of NCIE and MIE to multivariate variables is still based on the calculation of binary variables without actually calculating multivariate variables as a whole. The second one is the extension of bivariate measures, such as Dcor based on Euclidean distances between sample elements [ 13 , 14 ], and conditional multiinformation (CMI) for detecting the conditional dependence between multiple discrete variables [ 22 ]. Because they were not designed with equitability and generality as its goal, they performed poorly in these two areas.…”

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%