English

Metrics for more than two points at once

Adaptation and Self-Organizing Systems 2007-10-15 v1 Other Condensed Matter Machine Learning General Mathematics

Abstract

The conventional definition of a topological metric over a space specifies properties that must be obeyed by any measure of "how separated" two points in that space are. Here it is shown how to extend that definition, and in particular the triangle inequality, to concern arbitrary numbers of points. Such a measure of how separated the points within a collection are can be bootstrapped, to measure "how separated" from each other are two (or more) collections. The measure presented here also allows fractional membership of an element in a collection. This means it directly concerns measures of ``how spread out" a probability distribution over a space is. When such a measure is bootstrapped to compare two collections, it allows us to measure how separated two probability distributions are, or more generally, how separated a distribution of distributions is.

Keywords

Cite

@article{arxiv.nlin/0404032,
  title  = {Metrics for more than two points at once},
  author = {David H. Wolpert},
  journal= {arXiv preprint arXiv:nlin/0404032},
  year   = {2007}
}

Comments

8 pages

R2 v1 2026-07-22T18:12:11.383Z