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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 621–630 of 796 papers

TitleStatusHype
Recovery guarantee of weighted low-rank approximation via alternating minimization—0
A Note on Alternating Minimization Algorithm for the Matrix Completion Problem—0
Log-Normal Matrix Completion for Large Scale Link Prediction—0
Top-N Recommender System via Matrix Completion—0
Subspace Clustering Based Tag Sharing for Inductive Tag Matrix Refinement with Complex Errors—0
Song Recommendation with Non-Negative Matrix Factorization and Graph Total VariationCode0
Fitting Spectral Decay with the k-Support Norm—0
Matrix Completion Under Monotonic Single Index Models—0
New Perspectives on k-Support and Cluster Norms—0
Pseudo-Bayesian Robust PCA: Algorithms and Analyses—0
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