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https://issues.apache.org/jira/browse/MAHOUT-817?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13158157#comment-13158157
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Ted Dunning commented on MAHOUT-817:
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For the SSVD and PCA, what I had in mind was that forming an offset Y was easy
if you have the row means because you can compute
Y = (A - m) \Omega = A \Omega - m \Omega
That is, each row of Y can be adjusted on the fly as it is computed. The
computation of Q in the next step will be unchanged, but the definition of B
must include the mean subtraction as well:
B = Q' (A - m) = Q' A - Q' m
Other than this, the actual decomposition should be nearly good to go.
> 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
>
>
> 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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