Summary of Dynamic Angular Synchronization Under Smoothness Constraints, by Ernesto Araya et al.
Dynamic angular synchronization under smoothness constraints
by Ernesto Araya, Mihai Cucuringu, Hemant Tyagi
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 paper addresses a dynamic version of the classical angular synchronization problem, which involves recovering unknown angles from noisy pairwise measurements. In this scenario, both the angles and measurement graphs evolve over time points. Assuming a smoothness condition on the angle evolution, three algorithms are proposed for joint estimation across all time points. One algorithm is further analyzed for non-asymptotic recovery guarantees under different statistical models. Specifically, mean-squared error (MSE) convergence to zero is shown as the number of time points increases, even in settings with highly sparse and disconnected measurement graphs or large and increasing noise. Experiments on synthetic data are conducted to complement the theoretical results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a graph that shows how different things relate to each other. You want to figure out what directions these things are pointing in, but you only know some of this information because it’s been measured imperfectly. This problem is important in areas like computer vision and networking. In this paper, the researchers tackle a version of this problem where the graph and the directions change over time. They develop three ways to solve this problem and show that these methods work well even when the measurements are noisy or the graph is very sparse. The results are tested on fake data. |
Keywords
» Artificial intelligence » Mse » Synthetic data
