activity
20172020
most citedThe perturbation analysis of nonconvex low-rank matrix robust recovery

1 citations · 1 across the 2 of their papers we have counts for

collaborators

6 papers

cs.IT20201 cited

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…

cs.IT2020

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…

cs.IT2020

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…

stat.ML2019

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…

math.NA2019

Low-rank matrix recovery via regularized nuclear norm minimization

Wendong Wang, Feng Zhang, Jianjun Wang

In this paper, we theoretically investigate the low-rank matrix recovery problem in the context of the unconstrained regularized nuclear norm minimization (RNNM) framework. Our the…

eess.SP2017

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…