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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 251–260 of 796 papers

TitleStatusHype
Extended Gauss-Newton and ADMM-Gauss-Newton Algorithms for Low-Rank Matrix Optimization—0
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery—0
Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods—0
Factorizing LambdaMART for cold start recommendations—0
Deep Learning Approach for Matrix Completion Using Manifold Learning—0
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE—0
Autoencoder-based Graph Construction for Semi-supervised Learning—0
A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion—0
Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion—0
DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed With Deep Features—0
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