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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 201–210 of 796 papers

TitleStatusHype
Adaptive Noisy Matrix Completion—0
Lifelong Matrix Completion with Sparsity-Number—0
Flat minima generalize for low-rank matrix recovery—0
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
Indiscriminate Poisoning Attacks on Unsupervised Contrastive LearningCode1
Two-snapshot DOA Estimation via Hankel-structured Matrix Completion—0
Counterfactual inference for sequential experiments—0
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