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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 541–550 of 796 papers

TitleStatusHype
Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization—0
Mutual Kernel Matrix Completion—0
N^2: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion—0
Nearly-optimal Robust Matrix Completion—0
Nearly Optimal Robust Matrix Completion—0
Negative Binomial Matrix Completion—0
Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations—0
Network Inference by Learned Node-Specific Degree Prior—0
New Hardness Results for Low-Rank Matrix Completion—0
New Perspectives on k-Support and Cluster Norms—0
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