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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 776–796 of 796 papers

TitleStatusHype
Song Recommendation with Non-Negative Matrix Factorization and Graph Total VariationCode0
Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete DataCode0
Adaptive Matrix Completion for the Users and the Items in TailCode0
Robust PCA via Outlier PursuitCode0
Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix CompletionCode0
Matrix Completion via Nonconvex Regularization: Convergence of the Proximal Gradient AlgorithmCode0
Outlier-robust sparse/low-rank least-squares regression and robust matrix completionCode0
Forecasting Algorithms for Causal Inference with Panel DataCode0
Unsupervised Metric Learning in Presence of Missing DataCode0
Proximal Interacting Particle Langevin AlgorithmsCode0
Sequence-Aware Recommender SystemsCode0
An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU functionCode0
Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR SamplingCode0
Online Identification and Tracking of Subspaces from Highly Incomplete InformationCode0
A Perturbation Bound on the Subspace Estimator from Canonical ProjectionsCode0
Partial Trace Regression and Low-Rank Kraus DecompositionCode0
Collective Matrix CompletionCode0
Generalizing to Unseen Entities and Entity Pairs with Row-less Universal SchemaCode0
SweetRS: Dataset for a recommender systems of sweetsCode0
Geometric Matrix Completion: A Functional ViewCode0
Rotation Synchronization via Deep Matrix FactorizationCode0
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