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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 191–200 of 796 papers

TitleStatusHype
Dense Air Quality Maps Using Regressive Facility Location Based Drive By Sensing—0
Demystifying Language Model Forgetting with Low-rank Example Associations—0
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework—0
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion—0
Depth Enhancement via Low-rank Matrix Completion—0
Advancing Matrix Completion by Modeling Extra Structures beyond Low-Rankness—0
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization—0
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—0
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms—0
Deep Non-Rigid Structure from Motion with Missing Data—0
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