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 561–570 of 796 papers

TitleStatusHype
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview—0
Non-Convex Optimizations for Machine Learning with Theoretical Guarantee: Robust Matrix Completion and Neural Network Learning—0
Nonconvex Rectangular Matrix Completion via Gradient Descent without _2, Regularization—0
Nonlinear Inductive Matrix Completion based on One-layer Neural Networks—0
Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning—0
Patch Tracking-based Streaming Tensor Ring Completion for Visual Data Recovery—0
Non-Local Robust Quaternion Matrix Completion for Color Images and Videos Inpainting—0
Nonparametric Estimation of Low Rank Matrix Valued Function—0
Nonparametric Trace Regression in High Dimensions via Sign Series Representation—0
Norm-Bounded Low-Rank Adaptation—0
Show:102550
← PrevPage 57 of 80Next →

No leaderboard results yet.