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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 321–330 of 796 papers

TitleStatusHype
Concentration properties of fractional posterior in 1-bit matrix completion—0
Identifying global optimality for dictionary learning—0
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids—0
IHT-Inspired Neural Network for Single-Snapshot DOA Estimation with Sparse Linear Arrays—0
Concentration of tempered posteriors and of their variational approximations—0
Implicit bias of SGD in L_2-regularized linear DNNs: One-way jumps from high to low rank—0
A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization—0
A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic Data Recovery—0
Implicit Regularization in Deep Tensor Factorization—0
Adaptive Noisy Matrix Completion—0
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