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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
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework—0
Demystifying Language Model Forgetting with Low-rank Example Associations—0
Dense Air Quality Maps Using Regressive Facility Location Based Drive By Sensing—0
Depth-Aided Color Image Inpainting in Quaternion Domain—0
Depth Enhancement via Low-rank Matrix Completion—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization—0
A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings—0
Deterministic and Probabilistic Conditions for Finite Completability of Low-rank Multi-View Data—0
Color Image Inpainting via Robust Pure Quaternion Matrix Completion: Error Bound and Weighted Loss—0
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