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https://issues.apache.org/jira/browse/SPARK-23216?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-23216.
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Resolution: Incomplete
> Multiclass LogisticRegression could have methods like NCE, NEG, Hierarchical
> SoftMax, Blackout or IS
> ----------------------------------------------------------------------------------------------------
>
> Key: SPARK-23216
> URL: https://issues.apache.org/jira/browse/SPARK-23216
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Affects Versions: 2.2.1
> Reporter: Michel Lemay
> Priority: Minor
> Labels: bulk-closed
>
> When training a classifier with large number of classes, performance sink.
> This is expected when using regular (log)SoftMax methods to compute the loss
> since it needs to normalize current class score with the sum of all other
> classes score.
> I think this would be helpful to have approximate methods like Hierarchical
> SoftMax, NCE, NEG, IS to speedup training.
> A paper comparing different methods for approximate normalization over all
> classes:
> [http://web4.cs.ucl.ac.uk/staff/D.Barber/publications/AISTATS2017.pdf]
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