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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 601–610 of 796 papers

TitleStatusHype
Recognizing Emotions From Abstract Paintings Using Non-Linear Matrix CompletionCode0
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent—0
Fast Algorithms for Robust PCA via Gradient Descent—0
High resolution neural connectivity from incomplete tracing data using nonnegative spline regressionCode0
Riemannian stochastic variance reduced gradient on Grassmann manifoldCode0
Matrix Completion has No Spurious Local Minimum—0
A Riemannian gossip approach to decentralized matrix completion—0
Sub-Gaussian estimators of the mean of a random matrix with heavy-tailed entries—0
Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent—0
A note on the statistical view of matrix completion—0
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