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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 701–710 of 796 papers

TitleStatusHype
Errata: Distant Supervision for Relation Extraction with Matrix Completion—0
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems—0
Fast Exact Matrix Completion with Finite Samples—0
CUR Algorithm for Partially Observed Matrices—0
Noisy Matrix Completion under Sparse Factor Models—0
Generalized Conditional Gradient for Sparse Estimation—0
Individualized Rank Aggregation using Nuclear Norm Regularization—0
Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations—0
Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees—0
Adaptive Multinomial Matrix Completion—0
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