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https://issues.apache.org/jira/browse/MADLIB-1034?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15648630#comment-15648630
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Frank McQuillan commented on MADLIB-1034:
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Yes, docs could be clearer on this topic.
Regarding sparse vectors, k-means
http://madlib.incubator.apache.org/docs/latest/group__grp__kmeans.html
is currently the only module that explicitly accepts svec. There are other
svec specific functions provided as part of the svec module
http://madlib.incubator.apache.org/docs/latest/group__grp__svec.html
It is possible that svec might work with other modules if it is cast to a float
array like: `svec_input::double precision[]`
but I have not tested this much yet.
> Lacking documentation on use of Sparse Vectors with Supervised Learning Models
> ------------------------------------------------------------------------------
>
> Key: MADLIB-1034
> URL: https://issues.apache.org/jira/browse/MADLIB-1034
> Project: Apache MADlib
> Issue Type: Documentation
> Components: Documentation
> Reporter: Afshin Rahimi
> Fix For: v2.0
>
>
> There is documentation on supervised learning and sparse vectors separately.
> I couldn't find any documentation on using sparse vectors within the
> supervised learning models.
> Such documentation is particularly very helpful in text categorization where
> you can't create a lot of columns as described in the supervised learning
> documentation.
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