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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 631–640 of 796 papers

TitleStatusHype
Truncated Matrix Completion - An Empirical Study—0
Recognizing retinal ganglion cells in the dark—0
Recommendations from Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization—0
Recommendation via matrix completion using Kolmogorov complexity—0
Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders—0
Recovery guarantee of weighted low-rank approximation via alternating minimization—0
Recovery of damped exponentials using structured low rank matrix completion—0
Recovery of Piecewise Smooth Images from Few Fourier Samples—0
Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems—0
Reexamining Low Rank Matrix Factorization for Trace Norm Regularization—0
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