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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 761–770 of 796 papers

TitleStatusHype
Matrix Completion via Max-Norm Constrained Optimization—0
Missing Entries Matrix Approximation and Completion—0
Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion—0
Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation—0
Coherence and sufficient sampling densities for reconstruction in compressed sensing—0
Scaled Gradients on Grassmann Manifolds for Matrix Completion—0
Approximating Concavely Parameterized Optimization Problems—0
Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning—0
Calibrated Elastic Regularization in Matrix Completion—0
Relax and Randomize : From Value to Algorithms—0
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