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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 626–650 of 796 papers

TitleStatusHype
Song Recommendation with Non-Negative Matrix Factorization and Graph Total VariationCode0
Fitting Spectral Decay with the k-Support Norm—0
Matrix Completion Under Monotonic Single Index Models—0
New Perspectives on k-Support and Cluster Norms—0
Pseudo-Bayesian Robust PCA: Algorithms and Analyses—0
Fast Optimization Algorithm on Riemannian Manifolds and Its Application in Low-Rank Representation—0
Bayesian Matrix Completion via Adaptive Relaxed Spectral RegularizationCode0
Fast Low-Rank Matrix Learning with Nonconvex RegularizationCode0
Collaborative Filtering with Graph Information: Consistency and Scalable MethodsCode0
Recognizing retinal ganglion cells in the dark—0
Matrix Completion with Noisy Side Information—0
Secrets of Matrix Factorization: Approximations, Numerics, Manifold Optimization and Random Restarts—0
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms—0
An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion—0
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale DatasetCode0
Factorizing LambdaMART for cold start recommendations—0
The Singular Value Decomposition, Applications and Beyond—0
Minimax Lower Bounds for Noisy Matrix Completion Under Sparse Factor Models—0
Boolean Matrix Factorization and Noisy Completion via Message Passing—0
High-dimensional Time Series Prediction with Missing Values—0
Exponential Family Matrix Completion under Structural Constraints—0
Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees—0
Information-theoretic Bounds on Matrix Completion under Union of Subspaces Model—0
Regret Guarantees for Item-Item Collaborative Filtering—0
Preference Completion: Large-scale Collaborative Ranking from Pairwise ComparisonsCode0
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