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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 441–450 of 796 papers

TitleStatusHype
Learning Parameters for Weighted Matrix Completion via Empirical Estimation—0
Learning Transition Operators From Sparse Space-Time Samples—0
Learning Translations via Matrix Completion—0
Leave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions—0
Lifelong Matrix Completion with Sparsity-Number—0
Link Discovery using Graph Feature Tracking—0
Link Prediction via Matrix Completion—0
Local Search Algorithms for Rank-Constrained Convex Optimization—0
Log-Normal Matrix Completion for Large Scale Link Prediction—0
Low Permutation-rank Matrices: Structural Properties and Noisy Completion—0
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