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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 676–700 of 796 papers

TitleStatusHype
Abrupt Learning in Transformers: A Case Study on Matrix Completion—0
Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization—0
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms—0
Scalable Bayesian Non-linear Matrix Completion—0
Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal Riemannian Gradient—0
Universal Matrix Completion—0
Scalable Recommender Systems through Recursive Evidence Chains—0
Scalable Recommender Systemsthrough Recursive Evidence Chains—0
Scaled Gradients on Grassmann Manifolds for Matrix Completion—0
Scaled stochastic gradient descent for low-rank matrix completion—0
Secrets of Matrix Factorization: Approximations, Numerics, Manifold Optimization and Random Restarts—0
Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions—0
Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning—0
Semidefinite Programming versus Burer-Monteiro Factorization for Matrix Sensing—0
Sensor Network Localization via Riemannian Conjugate Gradient and Rank Reduction: An Extended Version—0
Sequence-aware item recommendations for multiply repeated user-item interactions—0
Waveform Design for OFDM-based ISAC Systems Under Resource Occupancy Constraint—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
Always Valid Risk Monitoring for Online Matrix Completion—0
A majorization-minimization algorithm for nonnegative binary matrix factorization—0
A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion—0
Convergence of the majorized PAM method with subspace correction for low-rank composite factorization model—0
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion—0
A More Stable Accelerated Gradient Method Inspired by Continuous-Time Perspective—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
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