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 241–250 of 796 papers

TitleStatusHype
Estimation of Missing Data in Intelligent Transportation System—0
Euclidean Distance Matrix Completion via Asymmetric Projected Gradient Descent—0
Exact Linear Convergence Rate Analysis for Low-Rank Symmetric Matrix Completion via Gradient Descent—0
Exact Reconstruction of Euclidean Distance Geometry Problem Using Low-rank Matrix Completion—0
A Novel Two-Step Method for Cross Language Representation Learning—0
Exact tensor completion with sum-of-squares—0
Exploiting Observation Bias to Improve Matrix Completion—0
Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints—0
Exponential Family Matrix Completion under Structural Constraints—0
1-bit Matrix Completion: PAC-Bayesian Analysis of a Variational Approximation—0
Show:102550
← PrevPage 25 of 80Next →

No leaderboard results yet.