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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 211–220 of 796 papers

TitleStatusHype
Deep Non-Rigid Structure from Motion with Missing Data—0
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion—0
Doubly Robust Inference in Causal Latent Factor Models—0
Doubly robust nearest neighbors in factor models—0
Background Subtraction via Fast Robust Matrix Completion—0
Dynamic matrix recovery from incomplete observations under an exact low-rank constraint—0
Effect of Beampattern on Matrix Completion with Sparse Arrays—0
Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation—0
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery—0
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion—0
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