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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 351–400 of 796 papers

TitleStatusHype
Inference and Uncertainty Quantification for Noisy Matrix Completion—0
Influenza Modeling Based on Massive Feature Engineering and International Flow Deconvolution—0
Information-theoretic Bounds on Matrix Completion under Union of Subspaces Model—0
Intelligent Reflecting Surface for Massive Device Connectivity: Joint Activity Detection and Channel Estimation—0
Which Tumblr Post Should I Read Next?—0
Interpretable Matrix Completion: A Discrete Optimization Approach—0
Introducing the Huber mechanism for differentially private low-rank matrix completion—0
Iterative missing value imputation based on feature importance—0
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System—0
Adaptively-weighted Nearest Neighbors for Matrix Completion—0
k-Space Deep Learning for Reference-free EPI Ghost Correction—0
L_2,1-Norm Regularized Quaternion Matrix Completion Using Sparse Representation and Quaternion QR Decomposition—0
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers—0
Large-Scale Matrix Factorization with Missing Data under Additional Constraints—0
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion—0
Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development—0
Learning Counterfactual Distributions via Kernel Nearest Neighbors—0
Learning from Ambiguously Labeled Face Images—0
Learning Iterative Reasoning through Energy Diffusion—0
Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion—0
Learning Mixtures of Discrete Product Distributions using Spectral Decompositions—0
Learning of Generalized Low-Rank Models: A Greedy Approach—0
Learning Parameters for Weighted Matrix Completion via Empirical Estimation—0
Learning Transition Operators From Sparse Space-Time Samples—0
Learning Translations via Matrix Completion—0
Leave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions—0
Lifelong Matrix Completion with Sparsity-Number—0
Active Feature Acquisition with Supervised Matrix Completion—0
Link Discovery using Graph Feature Tracking—0
Link Prediction via Matrix Completion—0
Local Search Algorithms for Rank-Constrained Convex Optimization—0
Log-Normal Matrix Completion for Large Scale Link Prediction—0
Sum-of-squares meets square loss: Fast rates for agnostic tensor completion—0
Low Permutation-rank Matrices: Structural Properties and Noisy Completion—0
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation—0
Low-Rank Approximations of Nonseparable Panel Models—0
Low-rank Bayesian matrix completion via geodesic Hamiltonian Monte Carlo on Stiefel manifolds—0
Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity—0
SUPER-Rec: SUrrounding Position-Enhanced Representation for Recommendation—0
Survey of Matrix Completion Algorithms—0
Low-rank matrix completion and denoising under Poisson noise—0
Low rank matrix completion and realization of graphs: results and problems—0
Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time—0
Low Rank Matrix Completion with Exponential Family Noise—0
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence—0
Low-rank matrix recovery with non-quadratic loss: projected gradient method and regularity projection oracle—0
Low-Rank Modeling and Its Applications in Image Analysis—0
Low-rank optimization for distance matrix completion—0
Low-rank optimization with trace norm penalty—0
Low Rank Quaternion Matrix Completion Based on Quaternion QR Decomposition and Sparse Regularizer—0
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