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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 461–470 of 796 papers

TitleStatusHype
Matrix Completion and Performance Guarantees for Single Individual Haplotyping—0
Causal Inference with Noisy and Missing Covariates via Matrix FactorizationCode0
k-Space Deep Learning for Reference-free EPI Ghost Correction—0
Nonlinear Inductive Matrix Completion based on One-layer Neural Networks—0
Scalable and Robust Community Detection with Randomized Sketching—0
Matrix Co-completion for Multi-label Classification with Missing Features and Labels—0
Communication-Efficient Projection-Free Algorithm for Distributed Optimization—0
Transduction with Matrix Completion Using Smoothed Rank Function—0
Projection-Free Bandit Convex Optimization—0
Extendable Neural Matrix Completion—0
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