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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 151–200 of 796 papers

TitleStatusHype
Concentration of tempered posteriors and of their variational approximations—0
Concentration properties of fractional posterior in 1-bit matrix completion—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects—0
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices—0
Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion—0
Conservative Stochastic Optimization with Expectation Constraints—0
Consistent Collective Matrix Completion under Joint Low Rank Structure—0
Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks—0
Constructing High Frequency Economic Indicators by Imputation—0
Approximate matrix completion based on cavity method—0
Contextual Bandits with Latent Confounders: An NMF Approach—0
Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent—0
Convergence of Gradient Descent with Small Initialization for Unregularized Matrix Completion—0
Convolutional Geometric Matrix Completion—0
A Sparse Interactive Model for Matrix Completion with Side Information—0
Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models—0
Covariates-Adjusted Mixed-Membership Estimation: A Novel Network Model with Optimal Guarantees—0
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly—0
Crowdsourcing with Sparsely Interacting Workers—0
CUR Algorithm for Partially Observed Matrices—0
CUR Algorithm with Incomplete Matrix Observation—0
Data-based system representations from irregularly measured data—0
Data-driven model selection within the matrix completion method for causal panel data models—0
Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Problems—0
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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