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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 191–200 of 796 papers

TitleStatusHype
Always Valid Risk Monitoring for Online Matrix Completion—0
A Generalized Latent Factor Model Approach to Mixed-data Matrix Completion with Entrywise ConsistencyCode0
Deep Linear Networks for Matrix Completion -- An Infinite Depth Limit—0
Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction—0
A Sequence-Aware Recommendation Method Based on Complex Networks—0
Bounded Simplex-Structured Matrix Factorization: Algorithms, Identifiability and ApplicationsCode0
Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity—0
Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems—0
Synthesis of Sparse Linear Arrays via Low-Rank Hankel Matrix Completion—0
Optimal (0,1)-Matrix Completion with Majorization Ordered Objectives (To the memory of Pravin Varaiya)—0
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