Summary of Structured Sampling For Robust Euclidean Distance Geometry, by Chandra Kundu et al.
Structured Sampling for Robust Euclidean Distance Geometry
by Chandra Kundu, Abiy Tasissa, HanQin Cai
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Optimization and Control (math.OC); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed algorithm uses Nyström method and robust principal component analysis to estimate the positions of points from distance measurements corrupted by sparse outliers. In this setting, anchor nodes have known distances to each other, while target nodes have complete but corrupted distance measurements to the anchors. The algorithm is computationally efficient, processing only a localized subset of the distance matrix, and achieves accurate recovery with a modest number of anchors even in the presence of high levels of sparse outliers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out where points are located based on distance measurements that might be wrong sometimes. It’s like trying to find your friends at a party by using GPS coordinates, but some of the coordinates are fake! The new way we do this uses special math tricks and is very fast. We tested it with pretend data and real-life science experiments and it works really well even when most of the measurements are wrong. |
Keywords
» Artificial intelligence » Principal component analysis
