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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 41–50 of 796 papers

TitleStatusHype
RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees—0
Adaptively-weighted Nearest Neighbors for Matrix Completion—0
Euclidean Distance Matrix Completion via Asymmetric Projected Gradient Descent—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
Truncated Matrix Completion - An Empirical Study—0
Computational Efficient Informative Nonignorable Matrix Completion: A Row- and Column-Wise Matrix U-Statistic Pseudo-Likelihood Approach—0
An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU functionCode0
Depth-Aided Color Image Inpainting in Quaternion Domain—0
Fast Two-photon Microscopy by Neuroimaging with Oblong Random Acquisition (NORA)—0
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems—0
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