Power weighted shortest paths for clustering Euclidean data
Published in Foundations of Data Science, 2019
Recommended citation: McKenzie, Daniel and Damelin, Steven. (2019). "Power weighted shortest paths for clustering Euclidean data. " Foundations of Data Science. 1(3). https://arxiv.org/abs/1905.13345
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