Robust Sparse Recovery in Impulsive Noise via M-Estimator and Non-Convex Regularization
نویسندگان
چکیده
منابع مشابه
Robust Sparse Recovery in Impulsive Noise via ℓp-ℓ1 Optimization
This paper addresses the issue of robust sparse recovery in compressive sensing (CS) in the presence of impulsive measurement noise. Recently, robust data-fitting models, such as 1 -norm, Lorentzian-norm, and Huber penalty function, have been employed to replace the popular 2 -norm loss model to gain more robust performance. In this paper, we propose a robust formulation for sparse recovery usi...
متن کاملNon-convex Sparse Regularization
We study the regularising properties of Tikhonov regularisation on the sequence space l with weighted, non-quadratic penalty term acting separately on the coefficients of a given sequence. We derive sufficient conditions for the penalty term that guarantee the well-posedness of the method, and investigate to which extent the same conditions are also necessary. A particular interest of this pape...
متن کاملRobust sparse recovery for compressive sensing in impulsive noise using ℓp-norm model fitting
This work considers the robust sparse recovery problem in compressive sensing (CS) in the presence of impulsive measurement noise. We propose a robust formulation for sparse recovery using the generalized `p-norm with 0 < p < 2 as the metric for the residual error under `1-norm regularization. An alternative direction method (ADM) has been proposed to solve this formulation efficiently. Moreove...
متن کاملFast, Robust and Non-convex Subspace Recovery
This work presents a fast and non-convex algorithm for robust subspace recovery. The data sets considered include inliers drawn around a low-dimensional subspace of a higher dimensional ambient space, and a possibly large portion of outliers that do not lie nearby this subspace. The proposed algorithm, which we refer to as Fast Median Subspace (FMS), is designed to robustly determine the underl...
متن کاملSparse Recovery by Non - Convex Optimization –
In this note, we address the theoretical properties of ∆p, a class of compressed sensing decoders that rely on ℓ p minimization with p ∈ (0, 1) to recover estimates of sparse and compressible signals from incomplete and inaccurate measurements. In particular, we extend the results of Candès, Romberg and Tao [3] and Wojtaszczyk [30] regarding the decoder ∆ 1 , based on ℓ 1 minimization, to ∆p wi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2901519