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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

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Showing 771–780 of 796 papers

TitleStatusHype
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
Fixed-rank matrix factorizations and Riemannian low-rank optimization—0
Low-rank optimization with trace norm penalty—0
RTRMC: A Riemannian trust-region method for low-rank matrix completion—0
SpaRCS: Recovering low-rank and sparse matrices from compressive measurements—0
A Denoising View of Matrix Completion—0
Penalty Decomposition Methods for Rank Minimization—0
Online Robust Subspace Tracking from Partial InformationCode0
Distributed Matrix Completion and Robust Factorization—0
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