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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
Spectral Geometric Matrix CompletionCode0
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery—0
Intelligent Reflecting Surface for Massive Device Connectivity: Joint Activity Detection and Channel Estimation—0
Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm AssumptionCode0
Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine LearningCode0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion—0
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift—0
Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data—0
New and Explicit Constructions of Unbalanced Ramanujan Bipartite Graphs—0
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