Weighted Geodesic Distance Following Fermat's Principle
Abstract: We propose a density-based estimator for weighted geodesic distances suitable for data lying on a manifold of lower dimension than ambient space and sampled from a possibly nonuniform distribution. After discussing its properties and implementation, we evaluate its performance as a tool for clustering tasks. A discussion on the consistency of the estimator is also given.
Keywords: distance learning, manifold learning, clustering.
TL;DR: We propose a new estimator for weighted geodesic distances that takes into account the underlying density of the data.
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