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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
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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