Summary of Sample-efficient Geometry Reconstruction From Euclidean Distances Using Non-convex Optimization, by Ipsita Ghosh et al.
Sample-Efficient Geometry Reconstruction from Euclidean Distances using Non-Convex Optimization
by Ipsita Ghosh, Abiy Tasissa, Christian Kümmerle
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper tackles a fundamental problem in machine learning that arises in various applications: finding point embeddings or geometric configurations given only Euclidean distance information. The authors propose a novel approach that leverages non-convex rank minimization and iteratively reweighted least squares (IRLS) to solve this problem, even with minimal distance samples. They establish a local convergence guarantee for their algorithm and demonstrate its data efficiency, scalability, and generalizability through numerical experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in machine learning that helps us understand how things are related when we only know how far they are from each other. The authors came up with a new way to do this using math techniques called non-convex rank minimization and iteratively reweighted least squares (IRLS). They tested their approach and showed it can work well even when we only have a few distance measurements. |
Keywords
» Artificial intelligence » Euclidean distance » Machine learning
