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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 401–425 of 796 papers

TitleStatusHype
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence—0
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation—0
On Low-rank Trace Regression under General Sampling DistributionCode0
Matrix Completion With Selective Sampling—0
Binary matrix completion with nonconvex regularizers—0
Multi-View Matrix Completion for Multi-Label Image Classification—0
Cluster Developing 1-Bit Matrix Completion—0
Ensemble Methods for Causal Effects in Panel Data Settings—0
Machine Learning Methods Economists Should Know About—0
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual PredictionCode0
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction—0
Provable Tensor Ring CompletionCode0
Matrix Completion via Nonconvex Regularization: Convergence of the Proximal Gradient AlgorithmCode0
Multispectral snapshot demosaicing via non-convex matrix completion—0
On the convex geometry of blind deconvolution and matrix completion—0
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization—0
Provable Low Rank Phase RetrievalCode0
Robust Matrix Completion State Estimation in Distribution Systems—0
Geometric Matrix Completion with Deep Conditional Random Fields—0
Computing large market equilibria using abstractions—0
Nonconvex Rectangular Matrix Completion via Gradient Descent without _2, Regularization—0
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion—0
Imputation and low-rank estimation with Missing Not At Random dataCode0
Matrix Completion under Low-Rank Missing Mechanism—0
Interpretable Matrix Completion: A Discrete Optimization Approach—0
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