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
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion—0
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion—0
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion—0
A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm—0
Low-rank matrix completion theory via Plucker coordinates—0
An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
Show:102550
← PrevPage 15 of 16Next →

No leaderboard results yet.