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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 251–275 of 796 papers

TitleStatusHype
Matrix Completion with Model-free Weighting—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few SamplesCode1
Patch Tracking-based Streaming Tensor Ring Completion for Visual Data Recovery—0
Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices—0
Low-Rank Hankel Tensor Completion for Traffic Speed EstimationCode0
Scalable and Explainable 1-Bit Matrix Completion via Graph Signal LearningCode1
Sharp Restricted Isometry Property Bounds for Low-rank Matrix Recovery Problems with Corrupted Measurements—0
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE—0
On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure—0
Nonparametric Trace Regression in High Dimensions via Sign Series Representation—0
Implicit Regularization in Deep Tensor Factorization—0
Matrix completion based on Gaussian parameterized belief propagation—0
Multi-target prediction for dummies using two-branch neural networks—0
PAC-Bayesian Matrix Completion with a Spectral Scaled Student Prior—0
NoisyCUR: An algorithm for two-cost budgeted matrix completionCode0
Deep Permutation Equivariant Structure from MotionCode1
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System—0
Adversarially-Trained Nonnegative Matrix FactorizationCode0
Simulation comparisons between Bayesian and de-biased estimators in low-rank matrix completionCode0
A Neural Network for SemigroupsCode0
Structure-Preserving Progressive Low-rank Image Completion for Defending Adversarial Attacks—0
Progresses and Challenges in Link Prediction—0
Implicit Regularization in Tensor FactorizationCode0
Policy Augmentation: An Exploration Strategy for Faster Convergence of Deep Reinforcement Learning AlgorithmsCode0
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