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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 241–250 of 796 papers

TitleStatusHype
Dense Air Quality Maps Using Regressive Facility Location Based Drive By Sensing—0
Variational Bayesian Filtering with Subspace Information for Extreme Spatio-Temporal Matrix Completion—0
Matrix Completion with Hierarchical Graph Side Information—0
On Asymptotic Linear Convergence of Projected Gradient Descent for Constrained Least Squares—0
A More Stable Accelerated Gradient Method Inspired by Continuous-Time Perspective—0
Fine-grained Generalization Analysis of Inductive Matrix Completion—0
PAC-Bayesian matrix completion with a spectral scaled Student prior—0
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction—0
Nonnegative Tensor Completion via Integer OptimizationCode0
Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers—0
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