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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 401–425 of 796 papers

TitleStatusHype
Low Rank Quaternion Matrix Recovery via Logarithmic Approximation—0
Widely Separated MIMO Radar Using Matrix Completion—0
Low-tubal-rank Tensor Completion using Alternating Minimization—0
LRSVRG-IMC: An SVRG-Based Algorithm for LowRank Inductive Matrix Completion—0
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion—0
Machine Learning Methods Economists Should Know About—0
Manopt, a Matlab toolbox for optimization on manifolds—0
Matrix Co-completion for Multi-label Classification with Missing Features and Labels—0
Matrix Coherence and the Nystrom Method—0
Matrix completion and extrapolation via kernel regression—0
A Comparison of Clustering and Missing Data Methods for Health Sciences—0
Matrix Completion and Performance Guarantees for Single Individual Haplotyping—0
Matrix Completion and Related Problems via Strong Duality—0
Matrix completion based on Gaussian parameterized belief propagation—0
Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data—0
Matrix Completion for Resolving Label Ambiguity—0
Matrix Completion for Structured Observations—0
Symmetric Matrix Completion with ReLU Sampling—0
Matrix Completion From any Given Set of Observations—0
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation—0
Matrix Completion from General Deterministic Sampling Patterns—0
Symmetric Tensor Completion from Multilinear Entries and Learning Product Mixtures over the Hypercube—0
Matrix Completion from Non-Uniformly Sampled Entries—0
Matrix Completion from O(n) Samples in Linear Time—0
Matrix Completion from Power-Law Distributed Samples—0
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