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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2016/11/05 02:26:58 UTC
[jira] [Commented] (SPARK-17748) One-pass algorithm for linear
regression with L1 and elastic-net penalties
[ https://issues.apache.org/jira/browse/SPARK-17748?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15638392#comment-15638392 ]
Apache Spark commented on SPARK-17748:
--------------------------------------
User 'jkbradley' has created a pull request for this issue:
https://github.com/apache/spark/pull/15779
> 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: Seth Hendrickson
> Fix For: 2.1.0
>
>
> 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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