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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 331–340 of 796 papers

TitleStatusHype
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion—0
A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic Data Recovery—0
Imposing Consistency Properties on Blackbox Systems with Applications to SVD-Based Recommender Systems—0
Improved Approximation Algorithms for Low-Rank Problems Using Semidefinite Optimization—0
Improving Matrix Completion by Exploiting Rating Ordinality in Graph Neural Networks—0
Improving Temporal Interpolation of Head and Body Pose using Gaussian Process Regression in a Matrix Completion Setting—0
Adaptive Noisy Matrix Completion—0
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction—0
Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images—0
Graph-Based Matrix Completion Applied to Weather Data—0
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