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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 601–625 of 796 papers

TitleStatusHype
On the Predictability of Human Assessment: when Matrix Completion Meets NLP Evaluation—0
On the properties of variational approximations of Gibbs posteriors—0
On the simplicity and conditioning of low rank semidefinite programs—0
On the Robustness of Cross-Concentrated Sampling for Matrix Completion—0
Optimal (0,1)-Matrix Completion with Majorization Ordered Objectives (To the memory of Pravin Varaiya)—0
Optimal Exact Matrix Completion Under new Parametrization—0
Optimal Algorithms for Latent Bandits with Cluster Structure—0
Disjunctive Branch-And-Bound for Certifiably Optimal Low-Rank Matrix Completion—0
Optimal Low-Rank Tensor Recovery from Separable Measurements: Four Contractions Suffice—0
Optimal Transfer Learning for Missing Not-at-Random Matrix Completion—0
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
Poisson Matrix Completion—0
Poisson Matrix Recovery and Completion—0
Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning—0
Polynomial Precision Dependence Solutions to Alignment Research Center Matrix Completion Problems—0
Power-Flow-Embedded Projection Conic Matrix Completion for Low-Observable Distribution Systems—0
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