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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-16105) PCA Reverse Transformer
Date Thu, 23 Jun 2016 15:13:16 GMT

    [ https://issues.apache.org/jira/browse/SPARK-16105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15346582#comment-15346582
] 

Sean Owen commented on SPARK-16105:
-----------------------------------

No, I understand that fine. I'm asking whether you actually need some additional special method
for this, because the principal components matrix lets you do this transformation pretty much
directly.

> PCA Reverse Transformer
> -----------------------
>
>                 Key: SPARK-16105
>                 URL: https://issues.apache.org/jira/browse/SPARK-16105
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML
>    Affects Versions: 1.6.1
>            Reporter: Stefan Panayotov
>            Priority: Minor
>
> The PCA class has a fit method that returns a PCAModel. One of the members of the PCAModel
is a pc (Principal Components Matrix). This matrix is available for inspection, but there
is no method to use this matrix for reverse transformation back to the original dimension.
For example, if I use the PCA to reduce dimensionality of my space from 96 to 15, I get a
96x15 pc Matrix. I can do some modeling in my reduced space and then I need to  reverse back
to the original 96 dimensional space. Basically, I need to multiply my 15 dimensional vectors
by the 96x15 pc Matrix to get back 96 dimensional vectors. Such method is missing from the
PCA model.



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