Abstract
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.
Original language | English (US) |
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Title of host publication | 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings |
Pages | 2102-2105 |
Number of pages | 4 |
DOIs | |
State | Published - Nov 8 2010 |
Event | 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States Duration: Mar 14 2010 → Mar 19 2010 |
Other
Other | 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 |
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Country/Territory | United States |
City | Dallas, TX |
Period | 3/14/10 → 3/19/10 |
All Science Journal Classification (ASJC) codes
- Software
- Signal Processing
- Electrical and Electronic Engineering