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Posted to dev@mahout.apache.org by "Dmitriy Lyubimov (Issue Comment Edited) (JIRA)" <ji...@apache.org> on 2011/12/06 00:38:40 UTC
[jira] [Issue Comment Edited] (MAHOUT-817) Add PCA options to SSVD
code
[ https://issues.apache.org/jira/browse/MAHOUT-817?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13163172#comment-13163172 ]
Dmitriy Lyubimov edited comment on MAHOUT-817 at 12/5/11 11:38 PM:
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So i did an R simulation of column-wise mean and it seems to work , so i think this verifies the math.
I still need to finish the doc (it also has a little typo in it), i will be finishing it from home as i don't seem to have the doc source on me here.
I guess it clears the implementation on existing ssvd solver.
test results comparing "brute forced" svd with "median propagated" version:
{code}
> respci$svalues
[1] 9.9995227 8.9992220 7.9907894 6.9860235 5.9786348 4.9866553 3.9853651
[8] 2.9735904 1.9999941 0.9971344
> ressvd$svalues
[1] 9.9995227 8.9992220 7.9907894 6.9860235 5.9786348 4.9866553 3.9853651
[8] 2.9735904 1.9999941 0.9971344
>
{code}
was (Author: dlyubimov):
So i did an R simulation of column-wise mean and it seems to work , so i think this verifies the math.
I still need to finish the doc (it also has a little typo in it), i will be finishing it from home as i don't seem to have the doc source on me here.
I guess it clears the implementation on existing ssvd solver.
> 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: Backlog
>
> Attachments: 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.
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