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Jianjun Wang

13 papers hereh-index 191.7k citations93 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author8
  • last author4

Across the 12 of 13 papers where every author was matched, so the position is known.

fields
  • cs.IT6
  • cs.LG2
  • math.NA2
  • cs.CV1
  • eess.SP1
  • stat.ML1
same name
  • Jianjun Wang — 4 papers, h 5
  • Jianjun Wang — 3 papers, h 42
  • Jianjun Wang — 3 papers, h 3
  • Jianjun Wang — 3 papers, h 47
  • Jianjun Wang — 2 papers, h 2
  • Jianjun Wang — 1 paper, h 9

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172023
most citedGuaranteed Tensor Recovery Fused Low-rankness and Smoothness

151 citations · 168 across the 12 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.IT2020★ 1 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 p-minimization to address a model of completely perturbed low-rank matrix recovery. The paper that based…

cs.IT2020

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…

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 δtr​<t/(4−t) 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…

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