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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 641–650 of 796 papers

TitleStatusHype
Reflection Removal Using Low-Rank Matrix Completion—0
Region-wise matching for image inpainting based on adaptive weighted low-rank decomposition—0
Regret Guarantees for Item-Item Collaborative Filtering—0
Regularization-free estimation in trace regression with symmetric positive semidefinite matrices—0
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning—0
Relax and Randomize : From Value to Algorithms—0
Relaxed Leverage Sampling for Low-rank Matrix Completion—0
Relevance Singular Vector Machine for low-rank matrix sensing—0
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion—0
Representational Transfer Learning for Matrix Completion—0
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