Summary of Subsampling, Aligning, and Averaging to Find Circular Coordinates in Recurrent Time Series, by Andrew J. Blumberg et al.
Subsampling, aligning, and averaging to find circular coordinates in recurrent time series
by Andrew J. Blumberg, Mathieu Carrière, Jun Hou Fung, Michael A. Mandell
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computational Geometry (cs.CG); Machine Learning (cs.LG); Algebraic Topology (math.AT)
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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 A novel algorithm for extracting robust circular coordinates from datasets exhibiting recurrence is proposed. This approach leverages techniques from simplicial complexes and dimension 1 cohomology classes to create circular coordinates on Rips complexes of datasets with prominent classes in their dimension 1 cohomology. The paper addresses the limitation of existing methods, which are sensitive to uneven sampling density. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to understand patterns in data that repeats itself, like brain recordings from tiny worms called C. elegans. Currently, there are methods to create circular shapes on complex networks based on a type of math problem. But these methods are very picky about how the data was collected. This paper tries to solve this problem by making the method work better. |
