Optimal Recovery of Missing Values for Non-Negative Matrix Factorization

Optimal Recovery of Missing Values for Non-Negative Matrix Factorization

Missing values imputation is often evaluated on some similarity measure between actual and imputed data. However, it may be more meaningful to evaluate downstream algorithm performance after imputation than the imputation itself. We describe a straightforward unsupervised imputation algorithm, a min...

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Bibliographic Details
Main Authors: Rebecca Chen Dean, Lav R. Varshney
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Open Journal of Signal Processing
Subjects:
Clustering
error bound
missing values
non-negative matrix factorization
Online Access:https://ieeexplore.ieee.org/document/9388875/

Internet

https://ieeexplore.ieee.org/document/9388875/

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