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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 681–690 of 796 papers

TitleStatusHype
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold—0
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis—0
Robust Egoistic Rigid Body Localization—0
Robust Low-rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method—0
Robust Low-Rank Matrix Completion via a New Sparsity-Inducing Regularizer—0
Spectral Geometric Matrix CompletionCode0
Provable Low Rank Phase RetrievalCode0
Collaborative Filtering with Graph Information: Consistency and Scalable MethodsCode0
Modeling longitudinal data using matrix completionCode0
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender SystemsCode0
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