Summary of Resampling and Averaging Coordinates on Data, by Andrew J. Blumberg et al.
Resampling and averaging coordinates on data
by Andrew J. Blumberg, Mathieu Carriere, Jun Hou Fung, Michael A. Mandell
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computational Geometry (cs.CG); Machine Learning (cs.LG)
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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 presents algorithms for computing intrinsic coordinates on point clouds, a crucial task in computer vision and robotics. The approach relies on generating multiple candidate coordinates by subsampling the data and varying hyperparameters of the embedding algorithm. A subset of representative embeddings is then identified through clustering and topological shape descriptors, before an average embedding is computed using generalized Procrustes analysis. The paper demonstrates robustness to noise and outliers on both synthetic and genomic datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand 3D shapes better. It’s like finding a special set of coordinates that can describe the shape of something, like a molecule or an object. The researchers developed new algorithms to do this, which works by looking at many different possible sets of coordinates and choosing the best ones. They tested it on some fake data and some real data from genetics, and it worked well even when there was noise or mistakes in the data. |
Keywords
* Artificial intelligence * Clustering * Embedding
