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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 601–625 of 796 papers

TitleStatusHype
Fast Algorithms for Robust PCA via Gradient Descent—0
High resolution neural connectivity from incomplete tracing data using nonnegative spline regressionCode0
Matrix Completion has No Spurious Local Minimum—0
Riemannian stochastic variance reduced gradient on Grassmann manifoldCode0
A Riemannian gossip approach to decentralized matrix completion—0
Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent—0
Sub-Gaussian estimators of the mean of a random matrix with heavy-tailed entries—0
A note on the statistical view of matrix completion—0
Dictionary Learning for Massive Matrix FactorizationCode0
Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient RegularizationCode0
Identifying global optimality for dictionary learning—0
1-bit Matrix Completion: PAC-Bayesian Analysis of a Variational Approximation—0
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion—0
Unified View of Matrix Completion under General Structural Constraints—0
Scaled stochastic gradient descent for low-rank matrix completion—0
Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images—0
A Harmonic Extension Approach for Collaborative Ranking—0
Network Inference by Learned Node-Specific Degree Prior—0
Recovery guarantee of weighted low-rank approximation via alternating minimization—0
A Note on Alternating Minimization Algorithm for the Matrix Completion Problem—0
Log-Normal Matrix Completion for Large Scale Link Prediction—0
Top-N Recommender System via Matrix Completion—0
Subspace Clustering Based Tag Sharing for Inductive Tag Matrix Refinement with Complex Errors—0
Song Recommendation with Non-Negative Matrix Factorization and Graph Total VariationCode0
Fitting Spectral Decay with the k-Support Norm—0
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