[jira] [Commented] (SPARK-2336) Approximate k-NN Models for MLLib

["longbao wang (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-2336?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14523226#comment-14523226 ] 

longbao wang commented on SPARK-2336:
-------------------------------------

I really agree with you，and i'm already implementing it,but i have a trouble,after build tree successful,you search target points' knn,so parallelize the input target points then search,but i think this have some questions,and one point's knn may in two partitions or more.

> Approximate k-NN Models for MLLib
> ---------------------------------
>
>                 Key: SPARK-2336
>                 URL: https://issues.apache.org/jira/browse/SPARK-2336
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Brian Gawalt
>            Priority: Minor
>              Labels: clustering, features
>
> After tackling the general k-Nearest Neighbor model as per https://issues.apache.org/jira/browse/SPARK-2335 , there's an opportunity to also offer approximate k-Nearest Neighbor. A promising approach would involve building a kd-tree variant within from each partition, a la
> http://www.autonlab.org/autonweb/14714.html?branch=1&language=2
> This could offer a simple non-linear ML model that can label new data with much lower latency than the plain-vanilla kNN versions.



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