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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
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion—0
A Unified Framework for Structured Low-rank Matrix Learning—0
Matrix Completion from Non-Uniformly Sampled Entries—0
Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion—0
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
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