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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 651–675 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
Matrix Completion for Resolving Label Ambiguity—0
A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm—0
Smooth PARAFAC Decomposition for Tensor Completion—0
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation—0
Optimal Low-Rank Tensor Recovery from Separable Measurements: Four Contractions Suffice—0
Prediction and Quantification of Individual Athletic Performance—0
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion—0
Social Trust Prediction via Max-norm Constrained 1-bit Matrix Completion—0
Regularization-free estimation in trace regression with symmetric positive semidefinite matrices—0
Poisson Matrix Recovery and Completion—0
Streaming, Memory Limited Matrix Completion with Noise—0
Structured Matrix Completion with Applications to Genomic Data Integration—0
Relaxed Leverage Sampling for Low-rank Matrix Completion—0
Online Matrix Completion and Online Robust PCA—0
Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal Riemannian Gradient—0
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