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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 176–200 of 796 papers

TitleStatusHype
Decentralized Singular Value Decomposition for Large-scale Distributed Sensor Networks—0
A Unified Convex Surrogate for the Schatten-p Norm—0
Always Valid Risk Monitoring for Online Matrix Completion—0
Column _2,0-norm regularized factorization model of low-rank matrix recovery and its computation—0
Deep geometric matrix completion: Are we doing it right?—0
A majorization-minimization algorithm for nonnegative binary matrix factorization—0
DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed With Deep Features—0
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE—0
Deep Learning Approach for Matrix Completion Using Manifold Learning—0
Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data—0
Deep Linear Networks for Matrix Completion -- An Infinite Depth Limit—0
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery—0
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion—0
Deep Non-Rigid Structure from Motion with Missing Data—0
Color Image Recovery Using Generalized Matrix Completion over Higher-Order Finite Dimensional Algebra—0
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework—0
Demystifying Language Model Forgetting with Low-rank Example Associations—0
Dense Air Quality Maps Using Regressive Facility Location Based Drive By Sensing—0
Depth-Aided Color Image Inpainting in Quaternion Domain—0
Depth Enhancement via Low-rank Matrix Completion—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization—0
A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings—0
Deterministic and Probabilistic Conditions for Finite Completability of Low-rank Multi-View Data—0
Color Image Inpainting via Robust Pure Quaternion Matrix Completion: Error Bound and Weighted Loss—0
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