3 citations · 5 across the 12 of their papers we have counts for
13 papers
Semi-Tensor Product-Based Multi-Term Randomized T-SVD and Its Visual Applications
Xingchen Xiao, Feng Zhang, Wenjin Qin +1
Tensor singular value decomposition (T-SVD), which is built upon the tensor-tensor product (t-product), has emerged as a powerful tool for processing high-dimensional visual data s…
Low-Rank Tensor Completion Based on Fractional Regularization with Ky Fan p-k Norm
Shan Fan, Feng Zhang, Jianjun Wang +2
This paper addresses low-rank tensor completion (LRTC) by proposing a novel nonconvex surrogate, namely the ratio of the tensor nuclear norm to the tensor Ky Fan p-k norm (TNPK), t…
Hyperspectral Anomaly Detection Fused Unified Nonconvex Tensor Ring Factors Regularization
Wenjin Qin, Hailin Wang, Hao Shu +5
In recent years, tensor decomposition-based approaches for hyperspectral anomaly detection (HAD) have gained significant attention in the field of remote sensing. However, existing…
Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation
Wenjin Qin, Hailin Wang, Feng Zhang +3
Within the tensor singular value decomposition (T-SVD) framework, existing robust low-rank tensor completion approaches have made great achievements in various areas of science and…
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…
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…
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…
Coherence-Based Performance Guarantee of Regularized -Norm Minimization and Beyond
Wendong Wang, Feng Zhang, Zhi Wang +1
In this paper, we consider recovering the signal from its few noisy measurements , where with …
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…