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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 151–158 of 158 papers

Title	Date	Tasks	Status
Simulation comparisons between Bayesian and de-biased estimators in low-rank matrix completion	Mar 22, 2021	Collaborative FilteringImputation	CodeCode Available
Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion	Apr 4, 2014	Low-Rank Matrix CompletionMatrix Completion	CodeCode Available
Provable Subspace Tracking from Missing Data and Matrix Completion	Oct 6, 2018	Low-Rank Matrix CompletionMatrix Completion	CodeCode Available
Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine Learning	Oct 27, 2019	BIG-bench Machine Learningcompressed sensing	CodeCode Available
Distant Supervision for Relation Extraction with Matrix Completion	Jun 1, 2014	Low-Rank Matrix CompletionMatrix Completion	CodeCode Available
GNMR: A provable one-line algorithm for low rank matrix recovery	Jun 24, 2021	Low-Rank Matrix CompletionMatrix Completion	CodeCode Available
Collaborative Filtering with Graph Information: Consistency and Scalable Methods	Dec 1, 2015	Collaborative FilteringLow-Rank Matrix Completion	CodeCode Available
Riemannian stochastic variance reduced gradient on Grassmann manifold	May 24, 2016	Low-Rank Matrix CompletionMatrix Completion	CodeCode Available

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