Quantifying Total Influence between Variables with Information Theoretic and Machine Learning Techniques

Quantifying Total Influence between Variables with Information Theoretic and Machine Learning Techniques

The increasingly sophisticated investigations of complex systems require more robust estimates of the correlations between the measured quantities. The traditional Pearson correlation coefficient is easy to calculate but sensitive only to linear correlations. The total influence between quantities i...

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Bibliographic Details
Main Authors: Andrea Murari, Riccardo Rossi, Michele Lungaroni, Pasquale Gaudio, Michela Gelfusa
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Entropy
Subjects:
machine learning tools
information theory
information quality ratio
total correlations
encoders
autoencoders
Online Access:https://www.mdpi.com/1099-4300/22/2/141

Internet

https://www.mdpi.com/1099-4300/22/2/141

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