Discovering Potential Correlations via Hypercontractivity
This addresses the need for better correlation discovery in scientific and practical applications, though it is incremental as it builds on existing information bottleneck concepts.
The paper tackled the problem of discovering hidden or potential correlations beyond average measures by proposing the hypercontractivity coefficient as a solution, and demonstrated its effectiveness through numerical experiments on WHO datasets and gene expression data, showing it is statistically more powerful than existing estimators.
Discovering a correlation from one variable to another variable is of fundamental scientific and practical interest. While existing correlation measures are suitable for discovering average correlation, they fail to discover hidden or potential correlations. To bridge this gap, (i) we postulate a set of natural axioms that we expect a measure of potential correlation to satisfy; (ii) we show that the rate of information bottleneck, i.e., the hypercontractivity coefficient, satisfies all the proposed axioms; (iii) we provide a novel estimator to estimate the hypercontractivity coefficient from samples; and (iv) we provide numerical experiments demonstrating that this proposed estimator discovers potential correlations among various indicators of WHO datasets, is robust in discovering gene interactions from gene expression time series data, and is statistically more powerful than the estimators for other correlation measures in binary hypothesis testing of canonical examples of potential correlations.