SOTAVerified

Matrix Completion

Matrix Completion is a method for recovering lost information. It originates from machine learning and usually deals with highly sparse matrices. Missing or unknown data is estimated using the low-rank matrix of the known data.

Source: A Fast Matrix-Completion-Based Approach for Recommendation Systems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 21–30 of 796 papers

TitleStatusHype
Covariates-Adjusted Mixed-Membership Estimation: A Novel Network Model with Optimal Guarantees—0
Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient DescentCode0
Norm-Bounded Low-Rank Adaptation—0
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size—0
Matrix Completion in Group Testing: Bounds and Simulations—0
Robust Egoistic Rigid Body Localization—0
Low rank matrix completion and realization of graphs: results and problems—0
Improved Approximation Algorithms for Low-Rank Problems Using Semidefinite Optimization—0
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery—0
Robust Matrix Completion for Discrete Rating-Scale DataCode0
Show:102550
← PrevPage 3 of 80Next →

No leaderboard results yet.