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 551–560 of 796 papers

TitleStatusHype
Online Optimization for Large-Scale Max-Norm Regularization—0
Online Optimization for Max-Norm Regularization—0
Online Policy Learning and Inference by Matrix Completion—0
The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion—0
Online Variational Bayesian Subspace Filtering with Applications—0
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation—0
On Tensor Completion via Nuclear Norm Minimization—0
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems—0
On the convex geometry of blind deconvolution and matrix completion—0
On the Fundamental Limits of Matrix Completion: Leveraging Hierarchical Similarity Graphs—0
Show:102550
← PrevPage 56 of 80Next →

No leaderboard results yet.