ArticleEntropy (Basel, Switzerland)2020
Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Predicting hydrocarbon presence in marine cold seep sediments using machine learning models trained with benthic bacterial 16S rRNA taxonomy.Microbiology spectrum · 2025Article
- Correlation and causation for cardiothoracic surgeons: part 4-distinguishing relationships in data.Indian journal of thoracic and cardiovascular surgery · 2025Article
- Associations of sunlight affinity with depression and sleep disorders in American males: Evidence from NHANES 2009-2020.PloS one · 2025Article
- Liquid Phase Exfoliation of 2D Materials and Its Electrochemical Applications in the Data-Driven Future.Precision chemistry · 2024Review
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Authors and funding
2 authors.
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Abstract
backgroundIn data analysis and machine learning, we often need to identify and quantify the correlation between variables. Although Pearson's correlation coefficient has been widely used, its value is reliable only for linear relationships and Distance correlation was introduced to address this shortcoming.
methodsDistance correlation can identify linear and nonlinear correlations. However, its performance drops in noisy conditions. In this paper, we introduce the Association Factor (AF) as a robust method for identification and quantification of linear and nonlinear associations in noisy conditions.
resultsTo test the performance of the proposed Association Factor, we modeled several simulations of linear and nonlinear relationships in different noise conditions and computed Pearson's correlation, Distance correlation, and the proposed Association Factor.
conclusionOur results show that the proposed method is robust in two ways. First, it can identify both linear and nonlinear associations. Second, the proposed Association Factor is reliable in both noiseless and noisy conditions.
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