Power Weighted Shortest Paths for Clustering Euclidean Data
This work addresses clustering challenges in high-dimensional data analysis, offering an incremental improvement with a new distance metric and fast algorithm.
The paper tackles the problem of clustering high-dimensional Euclidean data from disjoint low-dimensional manifolds by using power weighted shortest path distances, resulting in higher clustering accuracy as demonstrated theoretically and experimentally.
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.