Michel Lemay created SPARK-23216:
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Summary: 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
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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