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https://issues.apache.org/jira/browse/MAHOUT-817?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13158196#comment-13158196
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Dmitriy Lyubimov edited comment on MAHOUT-817 at 11/28/11 5:53 AM:
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Computation of m*omega also may be fairly involved because even that it is
vector matrix multiplication, Omega is dense, bigger than input, even though we
don't have to move its input around. Maybe for big inputs we can just take a
math expectation in of this. For the uniform distribution of murmur(is it
uniform?) we perhaps can ignore the whole m x Omega because it converges on 0
per law of big numbers.
was (Author: dlyubimov):
Computation of m*omega also may be fairly involved because even that is
vector matrix multiplication, Omega is dense, bigger than input, even though we
don't have to move its input around. Maybe for big inputs we can just take a
math expectation in of this. For the uniform distribution of murmur(is it
uniform?) we perhaps can ignore the whole m x Omega because it converges on 0
per law of big numbers.
> 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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