Threshold-free estimation of entropy from a Pearson matrix
H. Felippe, Aline Viol, Dráulio Barros de Araújo, M. G. E. da Luz, F. Palhano-Fontes, H. Onias, Ernesto P. Raposo, G. M. Viswanathan
Europhysics Letters January 24, 2023 DOI: 10.1209/0295-5075/acb5bd via OpenAlex
Summary
AI-generated from the abstractA new method directly estimates a unique entropy from a Pearson correlation matrix without requiring arbitrary thresholding. The approach rescales the matrix so it satisfies conditions for a von Neumann-like entropy. The method is demonstrated on neuroimaging time series from human brains under a psychedelic.
Study at a glance
| Characteristics | Methodological paper with demonstration Peer reviewed |
|---|---|
| Keywords | Thresholding Entropy estimation Statistics Artificial intelligence Pattern recognition psychology |
| Citations | 9 |
| Key finding | A unique entropy can be estimated directly from a Pearson correlation matrix without thresholding. |
Abstract
Abstract There is demand in diverse fields for a reliable method of estimating the entropy associated with correlations. The estimation of a unique entropy directly from the Pearson correlation matrix has remained an open problem for more than half a century. All existing approaches lack generality insofar as they require thresholding choices that arbitrarily remove possibly important information. Here we propose an objective procedure for directly estimating a unique entropy of a general Pearson matrix. We show that upon rescaling the Pearson matrix satisfies all necessary conditions for an analog of the von Neumann entropy to be well defined. No thresholding is required. We demonstrate the method by estimating the entropy from neuroimaging time series of the human brain under the influence of a psychedelic.