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Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions

Background: In 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 sh...

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Detalles Bibliográficos
Autores principales: Kachouie, Nezamoddin N., Deebani, Wejdan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516922/
https://www.ncbi.nlm.nih.gov/pubmed/33286214
http://dx.doi.org/10.3390/e22040440
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author Kachouie, Nezamoddin N.
Deebani, Wejdan
author_facet Kachouie, Nezamoddin N.
Deebani, Wejdan
author_sort Kachouie, Nezamoddin N.
collection PubMed
description Background: In 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. Methods: Distance 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. Results: To 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. Conclusion: Our 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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spelling pubmed-75169222020-11-09 Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions Kachouie, Nezamoddin N. Deebani, Wejdan Entropy (Basel) Article Background: In 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. Methods: Distance 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. Results: To 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. Conclusion: Our 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. MDPI 2020-04-13 /pmc/articles/PMC7516922/ /pubmed/33286214 http://dx.doi.org/10.3390/e22040440 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kachouie, Nezamoddin N.
Deebani, Wejdan
Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions
title Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions
title_full Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions
title_fullStr Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions
title_full_unstemmed Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions
title_short Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions
title_sort association factor for identifying linear and nonlinear correlations in noisy conditions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516922/
https://www.ncbi.nlm.nih.gov/pubmed/33286214
http://dx.doi.org/10.3390/e22040440
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