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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 81–90 of 796 papers

TitleStatusHype
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
Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image InpaintingCode1
Entry-Specific Bounds for Low-Rank Matrix Completion under Highly Non-Uniform Sampling—0
BlockEcho: Retaining Long-Range Dependencies for Imputing Block-Wise Missing Data—0
Discovering Abstract Symbolic Relations by Learning Unitary Group Representations—0
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