3 citations · 5 across the 10 of their papers we have counts for
11 papers
Robust Tensor Completion via Reflective Convolution Nuclear Norm Minimization
Weiguo Zhou, Feng Zhang, Wenjin Qin +1
Robust tensor completion recovers multidimensional data from partial observations corrupted by sparse gross errors. Existing convolutional low-rank models typically construct trans…
The high-order block RIP for non-convex block-sparse compressed sensing
Jianwen Huang, Xinling Liu, Jinyao Hou +1
This paper concentrates on the recovery of block-sparse signals, which is not only sparse but also nonzero elements are arrayed into some blocks (clusters) rather than being arbitr…
The perturbation analysis of nonconvex low-rank matrix robust recovery
Jianwen Huang, Wendong Wang, Feng Zhang +1
In this paper, we bring forward a completely perturbed nonconvex Schatten -minimization to address a model of completely perturbed low-rank matrix recovery. The paper that based…
The block mutual coherence property condition for signal recovery
Jianwen Huang, Hailin Wang, Feng Zhang +2
Compressed sensing shows that a sparse signal can stably be recovered from incomplete linear measurements. But, in practical applications, some signals have additional structure, w…
An Optimal Condition of Robust Low-rank Matrices Recovery
Jianwen Huang, Jianjun Wang, Feng Zhang +1
In this paper we investigate the reconstruction conditions of nuclear norm minimization for low-rank matrix recovery. We obtain sufficient conditions with $0<t<4/3…
Expansions of maximum and minimum from Generalized Maxwell distribution
Jianwen Huang
Generalized Maxwell distribution is an extension of the classic Maxwell distribution. In this paper, we concentrate on the joint distributional asymptotics of normalized maxima and…
On the distributional expansions of powered extremes from Maxwell distribution
Jianwen Huang, Xinling Liu, Jianjun Wang +3
In this paper, asymptotic expansions of the distributions and densities of powered extremes for Maxwell samples are considered. The results show that the convergence speeds of norm…
An analysis of noise folding for low-rank matrix recovery
Jianwen Huang, Jianjun Wang, Feng Zhang +2
Previous work regarding low-rank matrix recovery has concentrated on the scenarios in which the matrix is noise-free and the measurements are corrupted by noise. However, in practi…
RIP-based performance guarantee for low-tubal-rank tensor recovery
Feng Zhang, Wendong Wang, Jianwen Huang +2
The essential task of tensor data analysis focuses on the tensor decomposition and the corresponding notion of rank. In this paper, by introducing the notion of tensor Singular Val…
Tensor Restricted Isometry Property Analysis For a Large Class of Random Measurement Ensembles
Feng Zhang, Wendong Wang, Jingyao Hou +2
In previous work, theoretical analysis based on the tensor Restricted Isometry Property (t-RIP) established the robust recovery guarantees of a low-tubal-rank tensor. The obtained…
New sufficient conditions of signal recovery with tight frames via -analysis
Jianwen Huang, Jianjun Wang, Feng Zhang +1
The paper discusses the recovery of signals in the case that signals are nearly sparse with respect to a tight frame by means of the -analysis approach. We establish sever…