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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 631–640 of 796 papers

Title	Date	Tasks	Status
Fast Optimization Algorithm on Riemannian Manifolds and Its Application in Low-Rank Representation	Dec 7, 2015	Matrix Completion	—Unverified
Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization	Dec 3, 2015	Bayesian InferenceCollaborative Filtering	CodeCode Available
Fast Low-Rank Matrix Learning with Nonconvex Regularization	Dec 3, 2015	Matrix Completion	CodeCode Available
Collaborative Filtering with Graph Information: Consistency and Scalable Methods	Dec 1, 2015	Collaborative FilteringLow-Rank Matrix Completion	CodeCode Available
Recognizing retinal ganglion cells in the dark	Dec 1, 2015	Matrix Completion	—Unverified
Matrix Completion with Noisy Side Information	Dec 1, 2015	ClusteringMatrix Completion	—Unverified
Secrets of Matrix Factorization: Approximations, Numerics, Manifold Optimization and Random Restarts	Dec 1, 2015	Low-Rank Matrix CompletionMatrix Completion	—Unverified
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms	Dec 1, 2015	Matrix Completion	—Unverified
An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion	Nov 6, 2015	Low-Rank Matrix CompletionMatrix Completion	—Unverified
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset	Nov 4, 2015	Matrix Completion	CodeCode Available

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