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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 11–20 of 796 papers

TitleStatusHype
Computational Efficient Informative Nonignorable Matrix Completion: A Row- and Column-Wise Matrix U-Statistic Pseudo-Likelihood Approach—0
An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU functionCode0
Depth-Aided Color Image Inpainting in Quaternion Domain—0
Fast Two-photon Microscopy by Neuroimaging with Oblong Random Acquisition (NORA)—0
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems—0
Interference-Aware Edge Runtime Prediction with Conformal Matrix CompletionCode0
Optimal Transfer Learning for Missing Not-at-Random Matrix Completion—0
Recommendations from Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization—0
A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings—0
Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach—0
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