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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 651–700 of 796 papers

TitleStatusHype
Representation learning of drug and disease terms for drug repositioning—0
RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees—0
Riemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees—0
Riemannian Perspective on Matrix Factorization—0
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold—0
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis—0
Truncated Nuclear Norm Minimization for Image Restoration Based On Iterative Support Detection—0
Two-snapshot DOA Estimation via Hankel-structured Matrix Completion—0
Robust Egoistic Rigid Body Localization—0
Robust Low-rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method—0
Robust Low-Rank Matrix Completion via a New Sparsity-Inducing Regularizer—0
Uncertainty Quantification For Low-Rank Matrix Completion With Heterogeneous and Sub-Exponential Noise—0
Robust Matrix Completion State Estimation in Distribution Systems—0
Robust matrix completion via Novel M-estimator Functions—0
Robust Matrix Completion with Heavy-tailed Noise—0
Robust Matrix Completion with Mixed Data Types—0
On Robust Mean Estimation under Coordinate-level Corruption—0
Understanding Alternating Minimization for Matrix Completion—0
Robust Spectral Compressed Sensing via Structured Matrix Completion—0
Robust Spectral Detection of Global Structures in the Data by Learning a Regularization—0
Robust Task Clustering for Deep Many-Task Learning—0
Unified View of Matrix Completion under General Structural Constraints—0
RTRMC: A Riemannian trust-region method for low-rank matrix completion—0
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly—0
SAR: Semantic Analysis for Recommendation—0
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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