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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 591–600 of 796 papers

TitleStatusHype
Generalizing to Unseen Entities and Entity Pairs with Row-less Universal SchemaCode0
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method—0
Extended Gauss-Newton and ADMM-Gauss-Newton Algorithms for Low-Rank Matrix Optimization—0
Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization—0
Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions—0
Recognizing Emotions From Abstract Paintings Using Non-Linear Matrix CompletionCode0
Proximal Riemannian Pursuit for Large-Scale Trace-Norm Minimization—0
DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed With Deep Features—0
Contextual Bandits with Latent Confounders: An NMF Approach—0
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent—0
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