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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 141–150 of 796 papers

TitleStatusHype
Deep Collective Matrix Factorization for Augmented Multi-View LearningCode0
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural NetworksCode0
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power InjectionsCode0
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust RecommendationCode0
Fast Low-Rank Matrix Learning with Nonconvex RegularizationCode0
Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete DataCode0
Clipped Matrix Completion: A Remedy for Ceiling Effects—0
Characterization of the equivalence of robustification and regularization in linear and matrix regression—0
A Non-monotone Alternating Updating Method for A Class of Matrix Factorization Problems—0
CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS—0
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