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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 426–450 of 796 papers

TitleStatusHype
Matrix Factorization via Deep Learning—0
Mixture Matrix Completion—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
Deep Collective Matrix Factorization for Augmented Multi-View LearningCode0
Bayesian graph convolutional neural networks for semi-supervised classificationCode0
Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference—0
A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure—0
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
Scalable Recommender Systemsthrough Recursive Evidence Chains—0
Faster Matrix Completion Using Randomized SVDCode0
Provable Subspace Tracking from Missing Data and Matrix CompletionCode0
Training Complex Models with Multi-Task Weak SupervisionCode0
SAVOIAS: A Diverse, Multi-Category Visual Complexity DatasetCode1
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview—0
Modeling longitudinal data using matrix completionCode0
Matrix Completion with Weighted Constraint for Haplotype Estimation—0
Clipped Matrix Completion: A Remedy for Ceiling Effects—0
Multi-Target Prediction: A Unifying View on Problems and Methods—0
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine LearningCode0
Ranking with Features: Algorithm and A Graph Theoretic Analysis—0
OBOE: Collaborative Filtering for AutoML Model SelectionCode1
Improving Temporal Interpolation of Head and Body Pose using Gaussian Process Regression in a Matrix Completion Setting—0
Mixture Matrix Completion—0
Fusion Subspace Clustering: Full and Incomplete Data—0
Matrix completion and extrapolation via kernel regression—0
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