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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 171–180 of 796 papers

TitleStatusHype
A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion—0
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework—0
Sequence-aware item recommendations for multiply repeated user-item interactions—0
Region-wise matching for image inpainting based on adaptive weighted low-rank decomposition—0
Constructing High Frequency Economic Indicators by Imputation—0
Can Learning Be Explained By Local Optimality In Robust Low-rank Matrix Recovery?—0
Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time—0
Transductive Matrix Completion with Calibration for Multi-Task Learning—0
Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations—0
Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form SolutionCode0
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