Pearson Distance is not a Distance
Methodology
2019-08-19 v1 Machine Learning
Abstract
The Pearson distance between a pair of random variables with correlation , namely, 1-, has gained widespread use, particularly for clustering, in areas such as gene expression analysis, brain imaging and cyber security. In all these applications it is implicitly assumed/required that the distance measures be metrics, thus satisfying the triangle inequality. We show however, that Pearson distance is not a metric. We go on to show that this can be repaired by recalling the result, (well known in other literature) that is a metric. We similarly show that a related measure of interest, , which is invariant to the sign of , is not a metric but that is. We also give generalizations of these results.
Keywords
Cite
@article{arxiv.1908.06029,
title = {Pearson Distance is not a Distance},
author = {Victor Solo},
journal= {arXiv preprint arXiv:1908.06029},
year = {2019}
}