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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 121–130 of 158 papers

Title	Date	Tasks	Status
Phase transitions and sample complexity in Bayes-optimal matrix factorization	Feb 6, 2014	blind source separationDictionary Learning	—Unverified
Practical Matrix Completion and Corruption Recovery using Proximal Alternating Robust Subspace Minimization	Sep 6, 2013	Low-Rank Matrix CompletionMatrix Completion	—Unverified
Probabilistic low-rank matrix completion on finite alphabets	Dec 8, 2014	Dimensionality ReductionLow-Rank Matrix Completion	—Unverified
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms	Dec 1, 2013	Low-Rank Matrix CompletionMatrix Completion	—Unverified
R3MC: A Riemannian three-factor algorithm for low-rank matrix completion	Jun 11, 2013	Low-Rank Matrix CompletionMatrix Completion	—Unverified
Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion	Jun 14, 2018	FormLow-Rank Matrix Completion	—Unverified
Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders	Oct 20, 2021	Low-Rank Matrix CompletionMatrix Completion	—Unverified
Recovery of damped exponentials using structured low rank matrix completion	Apr 14, 2017	Low-Rank Matrix CompletionMatrix Completion	—Unverified
Reflection Removal Using Low-Rank Matrix Completion	Jul 1, 2017	Low-Rank Matrix CompletionMatrix Completion	—Unverified
Relaxed Leverage Sampling for Low-rank Matrix Completion	Mar 22, 2015	Low-Rank Matrix CompletionMatrix Completion	—Unverified

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