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From "Dmitriy Lyubimov (Commented) (JIRA)" <>
Subject [jira] [Commented] (MAHOUT-817) Add PCA options to SSVD code
Date Mon, 28 Nov 2011 06:22:40 GMT


Dmitriy Lyubimov commented on MAHOUT-817:

Yes expectatiin is zero but variance is going to be big regardless of the input *size I think
unfortunately. So m Omega term is still a problem. For my problems itsnbrute force computation
will actually take more than e.g. squaringn my input. So it was first thought but I don't
think it is valid enough. So I withdraw this for now.
> Add PCA options to SSVD code
> ----------------------------
>                 Key: MAHOUT-817
>                 URL:
>             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

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