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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 601–650 of 796 papers

TitleStatusHype
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates—0
Probabilistic low-rank matrix completion on finite alphabets—0
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms—0
Prognostics of Surgical Site Infections using Dynamic Health Data—0
Progresses and Challenges in Link Prediction—0
Transductive Matrix Completion with Calibration for Multi-Task Learning—0
Projected Wirtinger Gradient Descent for Low-Rank Hankel Matrix Completion in Spectral Compressed Sensing—0
Projection-Free Algorithm for Stochastic Bi-level Optimization—0
Projection-Free Bandit Convex Optimization—0
Propagation Map Reconstruction via Interpolation Assisted Matrix Completion—0
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent—0
Provable Inductive Matrix Completion—0
Provable Low Rank Plus Sparse Matrix Separation Via Nonconvex Regularizers—0
Provable Non-linear Inductive Matrix Completion—0
Provable Tensor-Train Format Tensor Completion by Riemannian Optimization—0
Triple Simplex Matrix Completion for Expense Forecasting—0
Proximal Riemannian Pursuit for Large-Scale Trace-Norm Minimization—0
Pseudo-Bayesian Robust PCA: Algorithms and Analyses—0
PU Learning for Matrix Completion—0
Quantized Matrix Completion for Personalized Learning—0
Quantizing Heavy-tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery—0
Quaternion Matrix Completion Using Untrained Quaternion Convolutional Neural Network for Color Image Inpainting—0
Quaternion Optimized Model with Sparse Regularization for Color Image Recovery—0
R3MC: A Riemannian three-factor algorithm for low-rank matrix completion—0
Scalable and Robust Community Detection with Randomized Sketching—0
Matrices with Gaussian noise: optimal estimates for singular subspace perturbation—0
Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization—0
Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion—0
Ranking with Features: Algorithm and A Graph Theoretic Analysis—0
Recent Developments on Factor Models and its Applications in Econometric Learning—0
Truncated Matrix Completion - An Empirical Study—0
Recognizing retinal ganglion cells in the dark—0
Recommendations from Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization—0
Recommendation via matrix completion using Kolmogorov complexity—0
Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders—0
Recovery guarantee of weighted low-rank approximation via alternating minimization—0
Recovery of damped exponentials using structured low rank matrix completion—0
Recovery of Piecewise Smooth Images from Few Fourier Samples—0
Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems—0
Reexamining Low Rank Matrix Factorization for Trace Norm Regularization—0
Reflection Removal Using Low-Rank Matrix Completion—0
Region-wise matching for image inpainting based on adaptive weighted low-rank decomposition—0
Regret Guarantees for Item-Item Collaborative Filtering—0
Regularization-free estimation in trace regression with symmetric positive semidefinite matrices—0
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning—0
Relax and Randomize : From Value to Algorithms—0
Relaxed Leverage Sampling for Low-rank Matrix Completion—0
Relevance Singular Vector Machine for low-rank matrix sensing—0
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion—0
Representational Transfer Learning for Matrix Completion—0
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