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 41–50 of 158 papers

TitleStatusHype
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM—0
Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
Effect of Beampattern on Matrix Completion with Sparse Arrays—0
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
Discrete Aware Matrix Completion via Convexized _0-Norm Approximation—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction—0
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization—0
Show:102550
← PrevPage 5 of 16Next →

No leaderboard results yet.