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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 361–370 of 796 papers

TitleStatusHype
Bounded Manifold Completion—0
Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning—0
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework—0
Influenza Modeling Based on Massive Feature Engineering and International Flow Deconvolution—0
Community Detection and Matrix Completion with Social and Item Similarity Graphs—0
A Fast Matrix-Completion-Based Approach for Recommendation Systems—0
Provable Non-linear Inductive Matrix Completion—0
Matrix Completion using Kronecker Product Approximation—0
Spectral Geometric Matrix CompletionCode0
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery—0
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