10 citations · 26 across the 14 of their papers we have counts for
15 papers
A note on sharp oracle bounds for Slope and Lasso
Zhiyong Zhou
In this paper, we study the sharp oracle bounds for Slope and Lasso and generalize the results in Bellec et al. (2018) to allow the case that the parameter vector is not exactly sp…
A Unified Framework for Constructing Nonconvex Regularizations
Zhiyong Zhou
Over the past decades, many individual nonconvex methods have been proposed to achieve better sparse recovery performance in various scenarios. However, how to construct a valid no…
Sparse recovery based on the generalized error function
Zhiyong Zhou
In this paper, we propose a novel sparse recovery method based on the generalized error function. The penalty function introduced involves both the shape and the scale parameters,…
Block sparse signal recovery via minimizing the block -ratio sparsity
Zhiyong Zhou
In this paper, we propose a method for block sparse signal recovery that minimizes the block -ratio sparsity $\left(\lVert z\rVert_{2,1}/\lVert z\rVert_{2,q}\right)^{\frac{q}{q-…
Minimization of the -ratio sparsity with for signal recovery
Zhiyong Zhou, Jun Yu
In this paper, we propose a general scale invariant approach for sparse signal recovery via the minimization of the -ratio sparsity. When , both the theoretic…
Statistical inference for block sparsity of complex signals
Jianfeng Wang, Zhiyong Zhou, Jun Yu
Block sparsity is an important parameter in many algorithms to successfully recover block sparse signals under the framework of compressive sensing. However, it is often unknown an…
Enhanced block sparse signal recovery based on -ratio block constrained minimal singular values
Jianfeng Wang, Zhiyong Zhou, Jun Yu
In this paper we introduce the -ratio block constrained minimal singular values (BCMSV) as a new measure of measurement matrix in compressive sensing of block sparse/compressive…
On -ratio CMSV for sparse recovery
Zhiyong Zhou, Jun Yu
Sparse recovery aims to reconstruct an unknown spare or approximately sparse signal from significantly few noisy incoherent linear measurements. As a kind of computable incoherence…
Adaptive Algorithm for Sparse Signal Recovery
Fekadu L. Bayisa, Zhiyong Zhou, Ottmar Cronie +1
Spike and slab priors play a key role in inducing sparsity for sparse signal recovery. The use of such priors results in hard non-convex and mixed integer programming problems. Mos…
Sparse recovery based on q-ratio constrained minimal singular values
Zhiyong Zhou, Jun Yu
We study verifiable sufficient conditions and computable performance bounds for sparse recovery algorithms such as the Basis Pursuit, the Dantzig selector and the Lasso estimator,…
Recovery analysis for weighted mixed minimization with
Zhiyong Zhou, Jun Yu
We study the recovery conditions of weighted mixed minimization for block sparse signal reconstruction from compressed measurements when partial block…
Sparsity estimation in compressive sensing with application to MR images
Jianfeng Wang, Zhiyong Zhou, Anders Garpebring +1
The theory of compressive sensing (CS) asserts that an unknown signal can be accurately recovered from measurements with provided that $\…
Phaseless compressive sensing using partial support information
Zhiyong Zhou, Jun Yu
We study the recovery conditions of weighted minimization for real-valued signal reconstruction from phaseless compressive sensing measurements when partial support inform…
Stable and robust -constrained compressive sensing recovery via robust width property
Zhiyong Zhou, Jun Yu
We study the recovery results of -constrained compressive sensing (CS) with via robust width property and determine conditions on the number of measurements for s…
Estimation of block sparsity in compressive sensing
Zhiyong Zhou, Jun Yu
Explicitly using the block structure of the unknown signal can achieve better reconstruction performance in compressive sensing. Theoretically, an unknown signal with block structu…