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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 291–300 of 796 papers

TitleStatusHype
Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem—0
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices—0
Mixed Membership Graph Clustering via Systematic Edge QueryCode0
Non-Local Robust Quaternion Matrix Completion for Color Images and Videos Inpainting—0
Leveraged Matrix Completion with Noise—0
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension—0
Sparse Array Beamforming Design for Wideband Signal Models—0
Adversarial Robust Low Rank Matrix Estimation: Compressed Sensing and Matrix Completion—0
Low-Rank Approximations of Nonseparable Panel Models—0
An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex OptimizationCode0
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