13 citations · 13 across the 3 of their papers we have counts for
4 papers
Global Convergence Analysis of Vanilla Gradient Descent for Asymmetric Matrix Completion
Xu Zhang, Shuo Chen, Jinsheng Li +2
This paper investigates the asymmetric low-rank matrix completion problem, which can be formulated as an unconstrained non-convex optimization problem with a nonlinear least-square…
Fast and Provable Hankel Tensor Completion for Multi-measurement Spectral Compressed Sensing
Jinsheng Li, Xu Zhang, Shuang Wu +1
In this paper, we introduce a novel low-rank Hankel tensor completion approach to address the problem of multi-measurement spectral compressed sensing. By lifting the multiple sign…
Simpler Gradient Methods for Blind Super-Resolution with Lower Iteration Complexity
Jinsheng Li, Wei Cui, Xu Zhang
We study the problem of blind super-resolution, which can be formulated as a low-rank matrix recovery problem via vectorized Hankel lift (VHL). The previous gradient descent method…
Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel Factorization
Jinsheng Li, Wei Cui, Xu Zhang
Current spectral compressed sensing methods via Hankel matrix completion employ symmetric factorization to demonstrate the low-rank property of the Hankel matrix. However, previous…