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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 391–400 of 796 papers

TitleStatusHype
Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction—0
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift—0
Guaranteed Matrix Completion Under Multiple Linear Transformations—0
Guaranteed Matrix Completion via Non-convex Factorization—0
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension—0
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion—0
Hierarchical Clustering and Matrix Completion for the Reconstruction of World Input-Output Tables—0
Hierarchical Matrix Completion for the Prediction of Properties of Binary Mixtures—0
High Dimensional Factor Analysis with Weak Factors—0
High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization—0
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