Power Weighted Shortest Paths for Clustering Euclidean Data
Machine Learning
2019-09-05 v3 Machine Learning
Abstract
We study the use of power weighted shortest path distance functions for clustering high dimensional Euclidean data, under the assumption that the data is drawn from a collection of disjoint low dimensional manifolds. We argue, theoretically and experimentally, that this leads to higher clustering accuracy. We also present a fast algorithm for computing these distances.
Cite
@article{arxiv.1905.13345,
title = {Power Weighted Shortest Paths for Clustering Euclidean Data},
author = {Daniel Mckenzie and Steven Damelin},
journal= {arXiv preprint arXiv:1905.13345},
year = {2019}
}
Comments
24 pages. Final version. To appear in Foundations of Data Science