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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 311–320 of 796 papers

TitleStatusHype
Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction—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
High-dimensional Time Series Prediction with Missing Values—0
High-Rank Matrix Completion and Clustering under Self-Expressive Models—0
Concentration properties of fractional posterior in 1-bit matrix completion—0
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids—0
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