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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 611–620 of 796 papers

TitleStatusHype
Optimal Transport with Heterogeneously Missing Data—0
Optimized Waveform Design for OFDM-based ISAC Systems Under Limited Resource Occupancy—0
Optimum Codesign for Image Denoising Between Type-2 Fuzzy Identifier and Matrix Completion Denoiser—0
Orthogonal Inductive Matrix Completion—0
PAC-Bayesian matrix completion with a spectral scaled Student prior—0
Parametric Models for Mutual Kernel Matrix Completion—0
Partial Matrix Completion—0
Penalty Decomposition Methods for Rank Minimization—0
Perturbation Analysis of Randomized SVD and its Applications to Statistics—0
Phase transitions and sample complexity in Bayes-optimal matrix factorization—0
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