SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 141–150 of 158 papers

TitleStatusHype
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms—0
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion—0
Unsupervised Spectral Learning of WCFG as Low-rank Matrix Completion—0
Practical Matrix Completion and Corruption Recovery using Proximal Alternating Robust Subspace Minimization—0
Manopt, a Matlab toolbox for optimization on manifolds—0
R3MC: A Riemannian three-factor algorithm for low-rank matrix completion—0
Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion—0
Coherence and sufficient sampling densities for reconstruction in compressed sensing—0
Scaled Gradients on Grassmann Manifolds for Matrix Completion—0
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion—0
Show:102550
← PrevPage 15 of 16Next →

No leaderboard results yet.