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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 71–80 of 158 papers

Title	Date	Tasks	Status	Hype
Bounded Manifold Completion	Dec 19, 2019	Dimensionality ReductionImage Inpainting	—Unverified	0
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)	Dec 7, 2019	Computational EfficiencyImage Reconstruction	CodeCode Available	1
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery	Nov 13, 2019	Low-Rank Matrix CompletionMatrix Completion	—Unverified	0
Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine Learning	Oct 27, 2019	BIG-bench Machine Learningcompressed sensing	CodeCode Available	0
The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion	Oct 4, 2019	Image ReconstructionLow-Rank Matrix Completion	—Unverified	0
Low-rank matrix completion and denoising under Poisson noise	Jul 11, 2019	DenoisingLow-Rank Matrix Completion	—Unverified	0
A divide-and-conquer algorithm for binary matrix completion	Jul 9, 2019	Low-Rank Matrix CompletionMatrix Completion	—Unverified	0
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization	Jul 5, 2019	Low-Rank Matrix CompletionMatrix Completion	—Unverified	0
Efficiently escaping saddle points on manifolds	Jun 10, 2019	Low-Rank Matrix CompletionMatrix Completion	—Unverified	0
Guaranteed Matrix Completion Under Multiple Linear Transformations	Jun 1, 2019	Image InpaintingLow-Rank Matrix Completion	—Unverified	0

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