Arrow Research search
Back to NeurIPS

NeurIPS 2013

The Randomized Dependence Coefficient

Conference Paper Artificial Intelligence · Machine Learning

Abstract

We introduce the Randomized Dependence Coefficient (RDC), a measure of non-linear dependence between random variables of arbitrary dimension based on the Hirschfeld-Gebelein-Rényi Maximum Correlation Coefficient. RDC is defined in terms of correlation of random non-linear copula projections; it is invariant with respect to marginal distribution transformations, has low computational cost and is easy to implement: just five lines of R code, included at the end of the paper.

Authors

Keywords

No keywords are indexed for this paper.

Context

Venue
Annual Conference on Neural Information Processing Systems
Archive span
1987-2025
Indexed papers
30776
Paper id
34200467859296774
v2026.09.13