<i>L</i><sub>1/2 </sub>-Regularized Quantile Method for Sparse Phase Retrieval
نویسندگان
چکیده
The sparse phase retrieval aims to recover the signal from quadratic measurements. However, measurements are often affected by outliers and asymmetric distribution noise. This paper introduces a novel method that combines quantile regression L1/2-regularizer. It is non-convex, non-smooth, non-Lipschitz optimization problem. We propose an efficient algorithm based on Alternating Direction Methods of Multiplier (ADMM) solve corresponding Numerous numerical experiments show this can signals with fewer robust dense bounded noise Laplace
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ژورنال
عنوان ژورنال: Open Journal of Applied Sciences
سال: 2022
ISSN: ['2165-3917', '2165-3925']
DOI: https://doi.org/10.4236/ojapps.2022.1212147