Bridge detection and robust geodesics estimation via random walks

We propose an algorithm for detecting bridges and estimating geodesic distances from a set of noisy samples of an underlying manifold. Finding geodesics on a nearest neighbors graph is known to fail in the presence of bridges. Our method detects bridges using global statistics via a Markov random walk and denoises the nearest neighbors graph using “surrogate” weights. We show experimentally that our method outperforms methods based on local neighborhood statistics.

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Eugene Brevdo, Peter J. Ramadge
• Keywords: Geodesic Distances, Geodesics, ICASSP 2010, Nearest Neighbors, Signal Processing