3 citations · 4 across the 7 of their papers we have counts for
8 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…
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…
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…