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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 701–725 of 796 papers

TitleStatusHype
Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient RegularizationCode0
Causal Inference with Noisy and Missing Covariates via Matrix FactorizationCode0
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale DatasetCode0
Low-Rank Inducing Norms with Optimality InterpretationsCode0
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
NoisyCUR: An algorithm for two-cost budgeted matrix completionCode0
Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form SolutionCode0
Dictionary Learning for Massive Matrix FactorizationCode0
Online Robust Subspace Tracking from Partial InformationCode0
A Gradient Descent Algorithm on the Grassman Manifold for Matrix CompletionCode0
Guaranteed Rank Minimization via Singular Value ProjectionCode0
On Low-rank Trace Regression under General Sampling DistributionCode0
Distant Supervision for Relation Extraction with Matrix CompletionCode0
VIGAN: Missing View Imputation with Generative Adversarial NetworksCode0
A Generalized Latent Factor Model Approach to Mixed-data Matrix Completion with Entrywise ConsistencyCode0
Adversarially-Trained Nonnegative Matrix FactorizationCode0
Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix CompletionCode0
Scalable Probabilistic Matrix Factorization with Graph-Based PriorsCode0
Matrix Low-Rank Approximation For Policy Gradient MethodsCode0
Provable Tensor Ring CompletionCode0
High resolution neural connectivity from incomplete tracing data using nonnegative spline regressionCode0
Hybrid Recommender System based on AutoencodersCode0
Can We Predict Performance of Large Models across Vision-Language Tasks?Code0
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory DataCode0
Matrix Low-Rank Trust Region Policy OptimizationCode0
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