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https://issues.apache.org/jira/browse/SPARK-5016?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14306688#comment-14306688
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Manoj Kumar edited comment on SPARK-5016 at 2/5/15 8:09 AM:
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Hi, I would like to fix this (since I'm familiar to an extent with this part of
the code) and maybe we could merge this before the sparseinput issue.
1. As a heuristic, how large should k be?
2. By distribute do you mean, to store samples
(https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala#L140)
as a collection using sc.parallelize, so that it can be operated on paraalel
across k? What role does n_features have?
Thanks.
was (Author: mechcoder):
Hi, I would like to fix this (since I'm familiar to an extent with this part of
the code) and maybe we could merge this before the sparseinput issue.
1. As a heuristic, how large should k be?
2. By distribute do you mean, to store samples
(https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala#L140)
as a collection using sc.parallelize, so that it can be operated on paraalel
across k.
Thanks.
> GaussianMixtureEM should distribute matrix inverse for large numFeatures, k
> ---------------------------------------------------------------------------
>
> Key: SPARK-5016
> URL: https://issues.apache.org/jira/browse/SPARK-5016
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.2.0
> Reporter: Joseph K. Bradley
>
> If numFeatures or k are large, GMM EM should distribute the matrix inverse
> computation for Gaussian initialization.
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