You are viewing a plain text version of this content. The canonical link for it is here.
Posted to dev@mahout.apache.org by "Dmitriy Lyubimov (Updated) (JIRA)" <ji...@apache.org> on 2012/01/02 07:19:30 UTC
[jira] [Updated] (MAHOUT-817) Add PCA options to SSVD code
[ https://issues.apache.org/jira/browse/MAHOUT-817?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Dmitriy Lyubimov updated MAHOUT-817:
------------------------------------
Fix Version/s: 0.6
> Add PCA options to SSVD code
> ----------------------------
>
> Key: MAHOUT-817
> URL: https://issues.apache.org/jira/browse/MAHOUT-817
> Project: Mahout
> Issue Type: New Feature
> Affects Versions: 0.6
> Reporter: Dmitriy Lyubimov
> Assignee: Dmitriy Lyubimov
> Fix For: 0.6, Backlog
>
> Attachments: MAHOUT-817.patch, MAHOUT-817.patch, SSVD-PCA options.pdf, ssvd-tests.R, ssvd.R, ssvd.m
>
>
> It seems that a simple solution should exist to integrate PCA mean subtraction into SSVD algorithm without making it a pre-requisite step and also avoiding densifying the big input.
> Several approaches were suggested:
> 1) subtract mean off B
> 2) propagate mean vector deeper into algorithm algebraically where the data is already collapsed to smaller matrices
> 3) --?
> It needs some math done first . I'll take a stab at 1 and 2 but thoughts and math are welcome.
--
This message is automatically generated by JIRA.
If you think it was sent incorrectly, please contact your JIRA administrators: https://issues.apache.org/jira/secure/ContactAdministrators!default.jspa
For more information on JIRA, see: http://www.atlassian.com/software/jira