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 71–80 of 158 papers

TitleStatusHype
Low-rank optimization with trace norm penalty—0
Low-tubal-rank Tensor Completion using Alternating Minimization—0
Manopt, a Matlab toolbox for optimization on manifolds—0
Matrix Completion from General Deterministic Sampling Patterns—0
Matrix Completion from Power-Law Distributed Samples—0
Matrix Completion in Almost-Verification Time—0
Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes—0
Nearly-optimal Robust Matrix Completion—0
Nearly Optimal Robust Matrix Completion—0
New Hardness Results for Low-Rank Matrix Completion—0
Show:102550
← PrevPage 8 of 16Next →

No leaderboard results yet.