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

1 citations · 1 across the 3 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…

cs.IT2019

Deterministic Analysis of Weighted BPDN With Partially Known Support Information

Wendong Wang, Jianjun Wang

In this paper, with the aid of the powerful Restricted Isometry Constant (RIC), a deterministic (or say non-stochastic) analysis, which includes a series of sufficient conditions (…

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…