Power Weighted Shortest Paths for Clustering Euclidean Data

Daniel Mckenzie; Steven Damelin

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arXiv · 1905.13345

Power Weighted Shortest Paths for Clustering Euclidean Data

Daniel Mckenzie·Steven Damelin

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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.

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Daniel Mckenzie, Steven Damelin. 2019-05-30. Power Weighted Shortest Paths for Clustering Euclidean Data. https://arxiv.org/abs/1905.13345

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