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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 71–80 of 796 papers

TitleStatusHype
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
Online Policy Learning and Inference by Matrix Completion—0
Structured Conformal Inference for Matrix Completion with Applications to Group Recommender Systems—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
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