People Mover's Distance: Class level geometry using fast pairwise data adaptive transportation costs
Machine Learning
2017-07-04 v1 Applications
Abstract
We address the problem of defining a network graph on a large collection of classes. Each class is comprised of a collection of data points, sampled in a non i.i.d. way, from some unknown underlying distribution. The application we consider in this paper is a large scale high dimensional survey of people living in the US, and the question of how similar or different are the various counties in which these people live. We use a co-clustering diffusion metric to learn the underlying distribution of people, and build an approximate earth mover's distance algorithm using this data adaptive transportation cost.
Cite
@article{arxiv.1707.00514,
title = {People Mover's Distance: Class level geometry using fast pairwise data adaptive transportation costs},
author = {Alexander Cloninger and Brita Roy and Carley Riley and Harlan M. Krumholz},
journal= {arXiv preprint arXiv:1707.00514},
year = {2017}
}