WEIGHTED GEODESIC DISTANCE FOLLOWING FERMAT?S PRINCIPLE

SAPIENZA, FACUNDO; GROISMAN, PABLO; JONCKHEERE, MATTHIEU

Vancouver

Workshop; International Conference on Learning Representations; 2018

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