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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 671–680 of 796 papers

TitleStatusHype
Relaxed Leverage Sampling for Low-rank Matrix Completion—0
Online Matrix Completion and Online Robust PCA—0
Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal Riemannian Gradient—0
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—0
Matrix Completion with Noisy Entries and Outliers—0
1-Bit Matrix Completion under Exact Low-Rank Constraint—0
Low Rank Matrix Completion with Exponential Family Noise—0
Exact tensor completion using t-SVD—0
Speeding up Permutation Testing in Neuroimaging—0
Recovery of Piecewise Smooth Images from Few Fourier Samples—0
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