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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 701–725 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
A General Framework for Fast Stagewise Algorithms—0
Matrix Completion under Interval Uncertainty—0
Matrix Coherence and the Nystrom Method—0
Matrix Completion on GraphsCode0
Fast matrix completion without the condition number—0
On the Power of Adaptivity in Matrix Completion and Approximation—0
Relevance Singular Vector Machine for low-rank matrix sensing—0
Online Optimization for Large-Scale Max-Norm Regularization—0
Truncated Nuclear Norm Minimization for Image Restoration Based On Iterative Support Detection—0
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction—0
Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints—0
Bayesian matrix completion: prior specification—0
Depth Enhancement via Low-rank Matrix Completion—0
Spectral Unsupervised Parsing with Additive Tree Metrics—0
Distant Supervision for Relation Extraction with Matrix CompletionCode0
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