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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 651–660 of 796 papers

TitleStatusHype
Projected Wirtinger Gradient Descent for Low-Rank Hankel Matrix Completion in Spectral Compressed Sensing—0
Categorical Matrix Completion—0
Annotation Projection-based Representation Learning for Cross-lingual Dependency Parsing—0
Notes on Low-rank Matrix Factorization—0
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis—0
Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms—0
On the properties of variational approximations of Gibbs posteriors—0
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation—0
Image Tag Completion and Refinement by Subspace Clustering and Matrix Completion—0
Symmetric Tensor Completion from Multilinear Entries and Learning Product Mixtures over the Hypercube—0
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