Power Weighted Shortest Paths for Unsupervised Learning

05/30/2019

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by   Daniel Mckenzie, et al.

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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.