[jira] [Updated] (MAHOUT-1273) Single Pass Algorithm for Penalized Linear Regression on MapReduce

["Kun Yang (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MAHOUT-1273?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Kun Yang updated MAHOUT-1273:
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    Attachment: PenalizedLinear.pdf

Draft
                
> Single Pass Algorithm for Penalized Linear Regression on MapReduce
> ------------------------------------------------------------------
>
>                 Key: MAHOUT-1273
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-1273
>             Project: Mahout
>          Issue Type: New Feature
>            Reporter: Kun Yang
>         Attachments: PenalizedLinear.pdf
>
>   Original Estimate: 720h
>  Remaining Estimate: 720h
>
> Penalized linear regression such as Lasso, Elastic-net are widely used in machine learning, but there are no very efficient scalable implementations on MapReduce.
> The published distributed algorithms for solving this problem is either iterative (which is not good for MapReduce, see Steven Boyd's paper) or approximate (what if we need exact solutions, see Paralleled stochastic gradient descent); another disadvantage of these algorithms is that they can not do cross validation in the training phase, which requires a user-specified penalty parameter in advance. 
> My ideas can train the model with cross validation in a single pass. They are based on some simple observations.
> I have implemented the primitive version of this algorithm in Alpine Data Labs. Advanced features such as inner-mapper combiner are employed to reduce the network traffic in the shuffle phase.  

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