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From "Joseph K. Bradley (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-1673) GLMNET implementation in Spark
Date Thu, 26 Feb 2015 20:05:04 GMT

    [ https://issues.apache.org/jira/browse/SPARK-1673?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14339062#comment-14339062
] 

Joseph K. Bradley commented on SPARK-1673:
------------------------------------------

Some thoughts:

{quote}
Friedman says in his paper that they found problems where glmnet would generate the entire
coefficient path more rapidly than sophisticated single point methods would generate single
point solutions
{quote}

This is true, but it's actually often even better to use an approximate path instead of an
exact path (which glmnet uses).  There is a lot of literature discussing "continuation," "warm-starts,"
"approximate regularization paths," and "homotopy" (which is sometimes overloaded to mean
approximate homotopy).  I worry about glmnet doing a lot of iterations, whereas analogous
but approximate methods could make larger jumps along the regularization path.

Continuation (following an approximate regularization path) can actually be used as a wrapper
around a lot of optimization algorithms to speed them up; I've used it successfully with coordinate
descent, accelerated gradient, and others.  I haven't tried it with OWL-QN.  It might be interesting
to explore a general continuation wrapper.  Some of the other benefits you mention apply to
any algorithm wrapped with continuation (e.g., automatically choosing a starting point for
the penalty parameter).

> GLMNET implementation in Spark
> ------------------------------
>
>                 Key: SPARK-1673
>                 URL: https://issues.apache.org/jira/browse/SPARK-1673
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Sung Chung
>
> This is a Spark implementation of GLMNET by Jerome Friedman, Trevor Hastie, Rob Tibshirani.
> http://www.jstatsoft.org/v33/i01/paper
> It's a straightforward implementation of the Coordinate-Descent based L1/L2 regularized
linear models, including Linear/Logistic/Multinomial regressions.



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