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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
Matrix Completion Under Monotonic Single Index Models—0
New Perspectives on k-Support and Cluster Norms—0
Fast Optimization Algorithm on Riemannian Manifolds and Its Application in Low-Rank Representation—0
Pseudo-Bayesian Robust PCA: Algorithms and Analyses—0
Fast Low-Rank Matrix Learning with Nonconvex RegularizationCode0
Bayesian Matrix Completion via Adaptive Relaxed Spectral RegularizationCode0
Secrets of Matrix Factorization: Approximations, Numerics, Manifold Optimization and Random Restarts—0
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms—0
Recognizing retinal ganglion cells in the dark—0
Matrix Completion with Noisy Side Information—0
Collaborative Filtering with Graph Information: Consistency and Scalable MethodsCode0
An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion—0
Factorizing LambdaMART for cold start recommendations—0
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale DatasetCode0
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
Projected Wirtinger Gradient Descent for Low-Rank Hankel Matrix Completion in Spectral Compressed Sensing—0
Categorical Matrix Completion—0
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