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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 101–110 of 796 papers

TitleStatusHype
Linear Recursive Feature Machines provably recover low-rank matricesCode1
Polynomial Precision Dependence Solutions to Alignment Research Center Matrix Completion Problems—0
Misclassification excess risk bounds for 1-bit matrix completion—0
Waveform Design for OFDM-based ISAC Systems Under Resource Occupancy Constraint—0
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion—0
Multiple Testing of Linear Forms for Noisy Matrix Completion—0
Iterative missing value imputation based on feature importance—0
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion—0
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints—0
Triple Simplex Matrix Completion for Expense Forecasting—0
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