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https://issues.apache.org/jira/browse/SPARK-2336?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14583886#comment-14583886
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Debasish Das edited comment on SPARK-2336 at 6/12/15 6:51 PM:
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Very cool idea Sen. Did you also look into FLANN for randomized KDTree and
KMeansTree. We have a PR for rowSimilarities
https://github.com/apache/spark/pull/6213 for brute force KNN generation which
we will use to compare the QoR of your PR as soon as you open up a stable
version.
was (Author: debasish83):
Very cool idea Sen. Did you also look into FLANN for randomized KDTree and
KMeansTree. We have a PR for rowSimilarities which we will use to compare the
QoR of your PR as soon as you open up a stable version.
> 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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