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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 231–240 of 796 papers

TitleStatusHype
Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning—0
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex MinimizationCode0
High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization—0
Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects—0
Two-snapshot DOA Estimation via Hankel-structured Matrix Completion—0
Splitting numerical integration for matrix completion—0
Counterfactual inference for sequential experiments—0
Color Image Inpainting via Robust Pure Quaternion Matrix Completion: Error Bound and Weighted Loss—0
Inductive Matrix Completion: No Bad Local Minima and a Fast AlgorithmCode0
LRSVRG-IMC: An SVRG-Based Algorithm for LowRank Inductive Matrix Completion—0
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