Preserving Cluster Features in Imputing High Dimensional Data with Extensive Missing Rate
Author:
Affiliation:
1. University of St. Thomas,Dept. Software & Data Science,St. Paul,MN,USA
2. Helmholtz Centre for Environmental Research / UFZ,Dept. Computational Biology,Leipzig,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10020192/10020156/10020732.pdf?arnumber=10020732
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4. Variational Autoencoders for Missing Data Imputation with Application to a Simulated Milling Circuit
5. The MovieLens Datasets: History and Context;harper;ACM Transactions on Interactive Intelligent Systems (TiiS),2015
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