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Low-Rank Matrix Completion

Low-Rank Matrix Completion is an important problem with several applications in areas such as recommendation systems, sketching, and quantum tomography. The goal in matrix completion is to recover a low rank matrix, given a small number of entries of the matrix.

Source: Universal Matrix Completion

Papers

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Showing 111–120 of 158 papers

Title	Date	Tasks	Status
RTRMC: A Riemannian trust-region method for low-rank matrix completion	Dec 1, 2011	Low-Rank Matrix CompletionMatrix Completion	—Unverified
Scaled Gradients on Grassmann Manifolds for Matrix Completion	Dec 1, 2012	Low-Rank Matrix CompletionMatrix Completion	—Unverified
Scaled stochastic gradient descent for low-rank matrix completion	Mar 16, 2016	Low-Rank Matrix CompletionMatrix Completion	—Unverified
Secrets of Matrix Factorization: Approximations, Numerics, Manifold Optimization and Random Restarts	Dec 1, 2015	Low-Rank Matrix CompletionMatrix Completion	—Unverified
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability	Feb 2, 2017	3D ReconstructionLow-Rank Matrix Completion	—Unverified
Sparse Array Beamformer Design for Active and Passive Sensing	Jan 18, 2021	Low-Rank Matrix CompletionMatrix Completion	—Unverified
Sparse Group Inductive Matrix Completion	Apr 27, 2018	feature selectionLow-Rank Matrix Completion	—Unverified
Static and Dynamic Robust PCA and Matrix Completion: A Review	Mar 1, 2018	Dimensionality ReductionLow-Rank Matrix Completion	—Unverified
Structured low-rank matrix completion for forecasting in time series analysis	Feb 22, 2018	Low-Rank Matrix CompletionMatrix Completion	—Unverified
Structure-Preserving Progressive Low-rank Image Completion for Defending Adversarial Attacks	Mar 4, 2021	Adversarial RobustnessLow-Rank Matrix Completion	—Unverified

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