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https://issues.apache.org/jira/browse/SPARK-17748?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-17748:
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Assignee: Apache Spark (was: Seth Hendrickson)
> One-pass algorithm for linear regression with L1 and elastic-net penalties
> --------------------------------------------------------------------------
>
> Key: SPARK-17748
> URL: https://issues.apache.org/jira/browse/SPARK-17748
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Seth Hendrickson
> Assignee: Apache Spark
>
> Currently linear regression uses weighted least squares to solve the normal
> equations locally on the driver when the dimensionality is small (<4096).
> Weighted least squares uses a Cholesky decomposition to solve the problem
> with L2 regularization (which has a closed-form solution). We can support
> L1/elasticnet penalties by solving the equations locally using OWL-QN solver.
> Also note that Cholesky does not handle singular covariance matrices, but
> L-BFGS and OWL-QN are capable of providing reasonable solutions. This patch
> can also add support for solving singular covariance matrices by also adding
> L-BFGS.
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