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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 501–525 of 796 papers

TitleStatusHype
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion—0
A New Theory for Matrix Completion—0
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation—0
Matrix Norm Estimation from a Few EntriesCode0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution—0
Sparse Inverse Covariance Estimation for Chordal Structures—0
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural NetworksCode0
A two-dimensional decomposition approach for matrix completion through gossip—0
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA—0
Background Subtraction via Fast Robust Matrix Completion—0
Sequential Matrix Completion—0
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization—0
SweetRS: Dataset for a recommender systems of sweetsCode0
Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective—0
Low Permutation-rank Matrices: Structural Properties and Noisy Completion—0
Robust Task Clustering for Deep Many-Task Learning—0
VIGAN: Missing View Imputation with Generative Adversarial NetworksCode0
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models—0
Nearly Optimal Robust Matrix Completion—0
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers—0
Efficient Low Rank Tensor Ring Completion—0
Recommendation via matrix completion using Kolmogorov complexity—0
Reflection Removal Using Low-Rank Matrix Completion—0
Concentration of tempered posteriors and of their variational approximations—0
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