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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 101–125 of 796 papers

TitleStatusHype
Convergence of the majorized PAM method with subspace correction for low-rank composite factorization model—0
Matrix Low-Rank Approximation For Policy Gradient MethodsCode0
Matrix Low-Rank Trust Region Policy OptimizationCode0
Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion—0
The radius of statistical efficiency—0
Subspace-Informed Matrix Completion—0
Efficient Federated Low Rank Matrix Completion—0
Discrete Aware Matrix Completion via Convexized _0-Norm Approximation—0
Structured Conformal Inference for Matrix Completion with Applications to Group Recommender Systems—0
Online Policy Learning and Inference by Matrix Completion—0
Concentration properties of fractional posterior in 1-bit matrix completion—0
Energy-modified Leverage Sampling for Radio Map Construction via Matrix Completion—0
Statistical Inference For Noisy Matrix Completion Incorporating Auxiliary Information—0
Matrix Completion via Nonsmooth Regularization of Fully Connected Neural Networks—0
Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel FactorizationCode0
Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion—0
Sensor Network Localization via Riemannian Conjugate Gradient and Rank Reduction: An Extended Version—0
Power-Flow-Embedded Projection Conic Matrix Completion for Low-Observable Distribution Systems—0
Improving Matrix Completion by Exploiting Rating Ordinality in Graph Neural Networks—0
BlockEcho: Retaining Long-Range Dependencies for Imputing Block-Wise Missing Data—0
Entry-Specific Bounds for Low-Rank Matrix Completion under Highly Non-Uniform Sampling—0
Discovering Abstract Symbolic Relations by Learning Unitary Group Representations—0
Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models—0
Doubly Robust Inference in Causal Latent Factor Models—0
Convergence of Gradient Descent with Small Initialization for Unregularized Matrix Completion—0
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