Signal Processing 2020 DOI: 10.1016/j.sigpro.2019.107369 View full text
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Yuli Sun, Xiang Tan, Xiao Li, Lin Lei, Gangyao Kuang

Abstract: In this paper, a s-difference type regularization for sparse recovery problem is proposed, which is the difference of the normal penalty function R (x) and its corresponding struncated function R (x s ). First, we show the equivalent conditions between the ℓ0 constrained problem and the unconstrained s-difference penalty regularized problem. Next, we choose the forward-backward splitting (FBS) method to solve the nonconvex regularizes function and further derive some closed-form solutions for the proximal map…

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