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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 201–210 of 796 papers

TitleStatusHype
Optimal (0,1)-Matrix Completion with Majorization Ordered Objectives (To the memory of Pravin Varaiya)—0
Online Low Rank Matrix Completion—0
Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems—0
Partial Matrix Completion—0
Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix CompletionCode0
Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR SamplingCode0
A Novel Plug-and-Play Approach for Adversarially Robust Generalization—0
A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic Data Recovery—0
Semidefinite Programming versus Burer-Monteiro Factorization for Matrix Sensing—0
Forecasting Algorithms for Causal Inference with Panel DataCode0
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