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20082026
most citedA Singular Value Thresholding Algorithm for Matrix Completion

134 citations · 644 across the 51 of their papers we have counts for

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Showing cs.ITShow all

16 papers · 1 filter

cs.IT2022★ 6 cited

Provable Sample-Efficient Sparse Phase Retrieval Initialized by Truncated Power Method

Jian-Feng Cai, Jingyang Li, Juntao You

We study the sparse phase retrieval problem, recovering an -sparse length- signal from magnitude-only measurements. Two-stage non-convex approaches have drawn much attent…

cs.IT2022★ 20 cited

Structured Gradient Descent for Fast Robust Low-Rank Hankel Matrix Completion

HanQin Cai, Jian-Feng Cai, Juntao You

We study the robust matrix completion problem for the low-rank Hankel matrix, which detects the sparse corruptions caused by extreme outliers while we try to recover the original H…

cs.IT2021★ 3 cited

Solving phase retrieval with random initial guess is nearly as good as by spectral initialization

Jianfeng Cai, Meng Huang, Dong Li +1

The problem of recovering a signal from a set of magnitude measurements is referred…

cs.IT2019★ 40 cited

Accelerated Structured Alternating Projections for Robust Spectrally Sparse Signal Recovery

HanQin Cai, Jian-Feng Cai, Tianming Wang +1

Consider a spectrally sparse signal that consists of complex sinusoids with or without damping. We study the robust recovery problem for the spectrally sparse…

cs.IT2018★ 4 cited

Towards the optimal construction of a loss function without spurious local minima for solving quadratic equations

Zhenzhen Li, Jian-Feng Cai, Ke Wei

The problem of finding a vector which obeys a set of quadratic equations , , plays an important role in many applications. In this paper we co…

cs.IT2017★ 7 cited

Separation-Free Super-Resolution from Compressed Measurements is Possible: an Orthonormal Atomic Norm Minimization Approach

Weiyu Xu, Jirong Yi, Soura Dasgupta +3

We consider the problem of recovering the superposition of distinct complex exponential functions from compressed non-uniform time-domain samples. Total Variation (TV) minimiza…