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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 771–780 of 796 papers

TitleStatusHype
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy—0
Solving the Robust Matrix Completion Problem via a System of Nonlinear Equations—0
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems—0
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability—0
CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS—0
Characterization of the equivalence of robustification and regularization in linear and matrix regression—0
Clipped Matrix Completion: A Remedy for Ceiling Effects—0
Cluster Developing 1-Bit Matrix Completion—0
Clustering of Nonnegative Data and an Application to Matrix Completion—0
Coherence and sufficient sampling densities for reconstruction in compressed sensing—0
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