Summary of A Sample Efficient Alternating Minimization-based Algorithm For Robust Phase Retrieval, by Adarsh Barik et al.
A Sample Efficient Alternating Minimization-based Algorithm For Robust Phase Retrieval
by Adarsh Barik, Anand Krishna, Vincent Y. F. Tan
First submitted to arxiv on: 7 Sep 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 A novel approach to robust phase retrieval is proposed, which addresses the challenge of recovering an unknown signal from potentially corrupted magnitude-only linear measurements. The method, based on alternating minimization and an oracle solver, ensures convergence to the true signal and provides a polynomial rate of convergence dependent on the fraction of corrupted measurements. An efficient construction of the oracle is also presented under a sparse arbitrary outliers model, offering insights into the geometric properties of the loss landscape. This algorithm avoids computationally intensive spectral initialization and achieves nearly linear sample complexity. The proposed approach has potential applications in various fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We can recover an unknown signal from noisy measurements by using a new method that works even when some data is missing or incorrect. This method uses two steps: first, it finds the best solution to a tricky optimization problem, and then it refines this solution using a simple algorithm. This approach is much faster than previous methods and can work with a small amount of noise in the data. |
Keywords
» Artificial intelligence » Optimization
