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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 661–670 of 796 papers

TitleStatusHype
Smooth PARAFAC Decomposition for Tensor Completion—0
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation—0
Optimal Low-Rank Tensor Recovery from Separable Measurements: Four Contractions Suffice—0
Prediction and Quantification of Individual Athletic Performance—0
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion—0
Social Trust Prediction via Max-norm Constrained 1-bit Matrix Completion—0
Regularization-free estimation in trace regression with symmetric positive semidefinite matrices—0
Poisson Matrix Recovery and Completion—0
Streaming, Memory Limited Matrix Completion with Noise—0
Structured Matrix Completion with Applications to Genomic Data Integration—0
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