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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 721–730 of 796 papers

TitleStatusHype
Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints—0
Bayesian matrix completion: prior specification—0
Depth Enhancement via Low-rank Matrix Completion—0
Spectral Unsupervised Parsing with Additive Tree Metrics—0
Distant Supervision for Relation Extraction with Matrix CompletionCode0
On Tensor Completion via Nuclear Norm Minimization—0
A Comparison of Clustering and Missing Data Methods for Health Sciences—0
Geometric Inference for General High-Dimensional Linear Inverse Problems—0
Advancing Matrix Completion by Modeling Extra Structures beyond Low-Rankness—0
Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix CompletionCode0
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