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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 121–130 of 796 papers

TitleStatusHype
Causal Inference with Noisy and Missing Covariates via Matrix FactorizationCode0
A Generalized Latent Factor Model Approach to Mixed-data Matrix Completion with Entrywise ConsistencyCode0
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral FiltersCode0
Recognizing Emotions From Abstract Paintings Using Non-Linear Matrix CompletionCode0
Riemannian stochastic variance reduced gradient algorithm with retraction and vector transportCode0
Robust Matrix Completion for Discrete Rating-Scale DataCode0
Collaborative Filtering with Graph Information: Consistency and Scalable MethodsCode0
Collective Matrix CompletionCode0
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power InjectionsCode0
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale DatasetCode0
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