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https://issues.apache.org/jira/browse/MADLIB-1061?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Frank McQuillan updated MADLIB-1061:
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Description:
Follow on to
https://issues.apache.org/jira/browse/MADLIB-927
which uses brute force.
Determine other k-NN algos to implement. From
http://scikit-learn.org/stable/modules/neighbors.html
candidates are:
* K-D Tree
* Ball Tree
* Other?
This JIRA is to implement K-D tree.
was:
Follow on to
https://issues.apache.org/jira/browse/MADLIB-927
which uses brute force.
Determine other k-NN algos to implement. From
http://scikit-learn.org/stable/modules/neighbors.html
candidates are:
* K-D Tree
* Ball Tree
* Other?
Look at how to implement in a distributed way. Also may want to revisit
current brute force approach to see if there are improvements to make on
parallelism - testing is in serial currently.
> Additional computation methods for k-NN - kd tree
> -------------------------------------------------
>
> Key: MADLIB-1061
> URL: https://issues.apache.org/jira/browse/MADLIB-1061
> Project: Apache MADlib
> Issue Type: New Feature
> Components: k-NN
> Reporter: Frank McQuillan
> Assignee: Orhan Kislal
> Priority: Major
> Labels: starter
> Fix For: v1.16
>
> Attachments: Sheet1-KNN-perf-num-features.pdf,
> Sheet2-KNN-tree-construction.pdf, Sheet3-KNN-tree-depth.pdf
>
>
> Follow on to
> https://issues.apache.org/jira/browse/MADLIB-927
> which uses brute force.
> Determine other k-NN algos to implement. From
> http://scikit-learn.org/stable/modules/neighbors.html
> candidates are:
> * K-D Tree
> * Ball Tree
> * Other?
> This JIRA is to implement K-D tree.
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