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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 201–250 of 796 papers

TitleStatusHype
New and Explicit Constructions of Unbalanced Ramanujan Bipartite Graphs—0
Deterministic Symmetric Positive Semidefinite Matrix Completion—0
Depth Enhancement via Low-rank Matrix Completion—0
Differentially Private Matrix Completion Revisited—0
Discovering Abstract Symbolic Relations by Learning Unitary Group Representations—0
Discrete Aware Matrix Completion via Convexized _0-Norm Approximation—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
A More Stable Accelerated Gradient Method Inspired by Continuous-Time Perspective—0
Depth-Aided Color Image Inpainting in Quaternion Domain—0
Distributed Representations for Building Profiles of Users and Items from Text Reviews—0
Dense Air Quality Maps Using Regressive Facility Location Based Drive By Sensing—0
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion—0
Doubly Robust Inference in Causal Latent Factor Models—0
Doubly robust nearest neighbors in factor models—0
Demystifying Language Model Forgetting with Low-rank Example Associations—0
Dynamic matrix recovery from incomplete observations under an exact low-rank constraint—0
Effect of Beampattern on Matrix Completion with Sparse Arrays—0
Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation—0
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework—0
Efficient Federated Low Rank Matrix Completion—0
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction—0
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion—0
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion—0
Efficiently escaping saddle points on manifolds—0
Efficient MCMC Sampling for Bayesian Matrix Factorization by Breaking Posterior Symmetries—0
Advancing Matrix Completion by Modeling Extra Structures beyond Low-Rankness—0
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—0
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms—0
Deep Non-Rigid Structure from Motion with Missing Data—0
Efficient Rigid Body Localization based on Euclidean Distance Matrix Completion for AGV Positioning under Harsh Environment—0
Empirical Bayes Matrix Completion—0
Energy-modified Leverage Sampling for Radio Map Construction via Matrix Completion—0
Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem—0
Ensemble Methods for Causal Effects in Panel Data Settings—0
Background Subtraction via Fast Robust Matrix Completion—0
Entry-Specific Matrix Estimation under Arbitrary Sampling Patterns through the Lens of Network Flows—0
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery—0
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion—0
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion—0
Convergence of the majorized PAM method with subspace correction for low-rank composite factorization model—0
Estimation of Missing Data in Intelligent Transportation System—0
Euclidean Distance Matrix Completion via Asymmetric Projected Gradient Descent—0
Deep Linear Networks for Matrix Completion -- An Infinite Depth Limit—0
Exact Reconstruction of Euclidean Distance Geometry Problem Using Low-rank Matrix Completion—0
Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data—0
Exact tensor completion with sum-of-squares—0
Multi-target prediction for dummies using two-branch neural networks—0
Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints—0
Exponential Family Matrix Completion under Structural Constraints—0
Deep Learning Approach for Matrix Completion Using Manifold Learning—0
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