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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 611–620 of 796 papers

TitleStatusHype
Dictionary Learning for Massive Matrix FactorizationCode0
Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient RegularizationCode0
Identifying global optimality for dictionary learning—0
1-bit Matrix Completion: PAC-Bayesian Analysis of a Variational Approximation—0
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion—0
Unified View of Matrix Completion under General Structural Constraints—0
Scaled stochastic gradient descent for low-rank matrix completion—0
Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images—0
A Harmonic Extension Approach for Collaborative Ranking—0
Network Inference by Learned Node-Specific Degree Prior—0
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