[
https://issues.apache.org/jira/browse/MAHOUT-1273?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Suneel Marthi updated MAHOUT-1273:
----------------------------------
Resolution: Won't Fix
Status: Resolved (was: Patch Available)
No activity on this in a long while now. Marking this as 'Won't Fix'.
> Single Pass Algorithm for Penalized Linear Regression with Cross Validation
> on MapReduce
> ----------------------------------------------------------------------------------------
>
> Key: MAHOUT-1273
> URL: https://issues.apache.org/jira/browse/MAHOUT-1273
> Project: Mahout
> Issue Type: New Feature
> Affects Versions: 0.9
> Reporter: Kun Yang
> Labels: documentation, features, patch, test
> Fix For: 1.0
>
> Attachments: Algorithm and Numeric Stability.pdf, Examples.pdf,
> Manual and Example.pdf, Manual and Example.pdf, Notes.pdf,
> PenalizedLinear.pdf, PenalizedLinearRegression.patch, java files.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.
> The core algorithm is a modified version of coordinate descent (see J.
> Freedman's paper). They implemented a very efficient R package "glmnet",
> which is the de facto standard of penalized regression.
> I have implemented the primitive version of this algorithm in Alpine Data
> Labs.
--
This message was sent by Atlassian JIRA
(v6.2#6252)
