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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 171–180 of 796 papers

TitleStatusHype
CUR Algorithm for Partially Observed Matrices—0
CUR Algorithm with Incomplete Matrix Observation—0
Data-based system representations from irregularly measured data—0
Data-driven model selection within the matrix completion method for causal panel data models—0
Column _2,0-norm regularized factorization model of low-rank matrix recovery and its computation—0
Decentralized Singular Value Decomposition for Large-scale Distributed Sensor Networks—0
A Unified Convex Surrogate for the Schatten-p Norm—0
Always Valid Risk Monitoring for Online Matrix Completion—0
Color Image Recovery Using Generalized Matrix Completion over Higher-Order Finite Dimensional Algebra—0
A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings—0
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