Joseph K. Bradley created SPARK-6683:
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Summary: GLMs with GradientDescent could scale step size instead
of features
Key: SPARK-6683
URL: https://issues.apache.org/jira/browse/SPARK-6683
Project: Spark
Issue Type: Improvement
Components: MLlib
Affects Versions: 1.3.0
Reporter: Joseph K. Bradley
Priority: Minor
GeneralizedLinearAlgorithm can scale features. This improves optimization
behavior (and also affects the optimal solution, as is being discussed and
hopefully fixed by [https://github.com/apache/spark/pull/5055]).
This is a bit inefficient since it requires making a rescaled copy of the data.
GradientDescent could instead scale the step size separately for each feature
(and adjust regularization as needed; see the PR linked above). This would
require storing a vector of length numFeatures, rather than making a full copy
of the data.
I haven't thought this through for LBFGS, so I'm not sure if it's generally
usable or would require a specialization for GLMs with GradientDescent.
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