Detecting Trivariate Associations in High-Dimensional Datasets
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 coeffi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/7/2806 |
| _version_ | 1797437630589173760 |
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| author | Chuanlu Liu Shuliang Wang Hanning Yuan Yingxu Dang Xiaojia Liu |
| author_facet | Chuanlu Liu Shuliang Wang Hanning Yuan Yingxu Dang Xiaojia Liu |
| author_sort | Chuanlu Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T11:24:09Z |
| format | Article |
| id | doaj.art-32bef812371a46d7b3bd16f3bdf0abe7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:24:09Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-32bef812371a46d7b3bd16f3bdf0abe72023-12-01T00:05:59ZengMDPI AGSensors1424-82202022-04-01227280610.3390/s22072806Detecting Trivariate Associations in High-Dimensional DatasetsChuanlu Liu0Shuliang Wang1Hanning Yuan2Yingxu Dang3Xiaojia Liu4School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaDetecting 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.https://www.mdpi.com/1424-8220/22/7/2806quadratic optimized trivariate information coefficient (QOTIC)trivariate associationsmaximal information coefficient (MIC)correlationlarge data |
| spellingShingle | Chuanlu Liu Shuliang Wang Hanning Yuan Yingxu Dang Xiaojia Liu Detecting Trivariate Associations in High-Dimensional Datasets Sensors quadratic optimized trivariate information coefficient (QOTIC) trivariate associations maximal information coefficient (MIC) correlation large data |
| title | Detecting Trivariate Associations in High-Dimensional Datasets |
| title_full | Detecting Trivariate Associations in High-Dimensional Datasets |
| title_fullStr | Detecting Trivariate Associations in High-Dimensional Datasets |
| title_full_unstemmed | Detecting Trivariate Associations in High-Dimensional Datasets |
| title_short | Detecting Trivariate Associations in High-Dimensional Datasets |
| title_sort | detecting trivariate associations in high dimensional datasets |
| topic | quadratic optimized trivariate information coefficient (QOTIC) trivariate associations maximal information coefficient (MIC) correlation large data |
| url | https://www.mdpi.com/1424-8220/22/7/2806 |
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