# A Multi-Way Correlation Coefficient

arXiv: https://arxiv.org/abs/2003.02561 · HTML: https://mlsanity.com/paper/2003.02561/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks](https://mlsanity.com/paper/2307.02040/) (2023)
- [ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning](https://mlsanity.com/paper/2504.10851/) (2025)

## Abstract (arXiv)

Pearson's correlation is an important summary measure of the amount of dependence between two variables. It is natural to want to generalise the concept of correlation as a single number that measures the inter-relatedness of three or more variables e.g. how `correlated' are a collection of variables in which non are specifically to be treated as an `outcome'? In this short article, we introduce such a measure, and show that it reduces to the modulus of Pearson's r in the two dimensional case.