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- W4313233466 abstract "The sparse phase retrieval aims to recover the sparse signal from quadratic measurements. However, the measurements are often affected by outliers and asymmetric distribution noise. This paper introduces a novel method that combines the quantile regression and the L1/2-regularizer. It is a non-convex, non-smooth, non-Lipschitz optimization problem. We propose an efficient algorithm based on the Alternating Direction Methods of Multiplier (ADMM) to solve the corresponding optimization problem. Numerous numerical experiments show that this method can recover sparse signals with fewer measurements and is robust to dense bounded noise and Laplace noise." @default.
- W4313233466 created "2023-01-06" @default.
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- W4313233466 date "2022-01-01" @default.
- W4313233466 modified "2023-10-01" @default.
- W4313233466 title "<i>L</i><sub>1/2 </sub>-Regularized Quantile Method for Sparse Phase Retrieval" @default.
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- W4313233466 doi "https://doi.org/10.4236/ojapps.2022.1212147" @default.
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