6 papers
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…
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…
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…
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 (…
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…
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…