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https://issues.apache.org/jira/browse/SPARK-18060?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Nick Pentreath updated SPARK-18060:
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Assignee: Seth Hendrickson
Target Version/s: 2.1.0
> Avoid unnecessary standardization in multinomial logistic regression training
> -----------------------------------------------------------------------------
>
> Key: SPARK-18060
> URL: https://issues.apache.org/jira/browse/SPARK-18060
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Reporter: Seth Hendrickson
> Assignee: Seth Hendrickson
>
> The MLOR implementation in spark.ml trains the model in the standardized
> feature space by dividing the feature values by the column standard deviation
> in each iteration. We perform this computation many time more than is
> necessary in order to achieve sequential memory access pattern when computing
> the gradients. We can have both - sequential access patterns and reduced
> computation - if we use a column major layout for the coefficients.
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