[jira] [Commented] (MAHOUT-817) Add PCA options to SSVD code

["Dmitriy Lyubimov (Commented) (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MAHOUT-817?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13163192#comment-13163192 ] 

Dmitriy Lyubimov commented on MAHOUT-817:
-----------------------------------------

and i also don't see any difference for small 100x200 inputs between pci and svd on a fixed(mean subtracted) input even if bypass median correction for Ys in both B_0 and power iterations!.. 

perhaps it has to do with the way i generate the input. that also may not necessarily be the case for extreme sparse cases. 

But i think first patch could bypass the Y fix.

{code}
 respci$svalues
 [1] 9.9013440 8.9980801 7.9936265 6.9882617 5.9982148 4.9935232 3.9848657
 [8] 2.9811621 1.9891654 0.9977757
> ressvd$svalues
 [1] 9.9013440 8.9980801 7.9936265 6.9882617 5.9982148 4.9935232 3.9848657
 [8] 2.9811621 1.9891654 0.9977757
> 
{code}
                
> 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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