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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 551–575 of 796 papers

TitleStatusHype
Online Optimization for Large-Scale Max-Norm Regularization—0
Online Optimization for Max-Norm Regularization—0
Online Policy Learning and Inference by Matrix Completion—0
The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion—0
Online Variational Bayesian Subspace Filtering with Applications—0
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation—0
On Tensor Completion via Nuclear Norm Minimization—0
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems—0
On the convex geometry of blind deconvolution and matrix completion—0
On the Fundamental Limits of Matrix Completion: Leveraging Hierarchical Similarity Graphs—0
On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure—0
Can Learning Be Explained By Local Optimality In Robust Low-rank Matrix Recovery?—0
On the Power of Adaptivity in Matrix Completion and Approximation—0
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems—0
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
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