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Posted to issues@spark.apache.org by "Feynman Liang (JIRA)" <ji...@apache.org> on 2015/07/01 00:37:04 UTC

[jira] [Commented] (SPARK-5016) GaussianMixtureEM should distribute matrix inverse for large numFeatures, k

    [ https://issues.apache.org/jira/browse/SPARK-5016?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14609194#comment-14609194 ] 

Feynman Liang commented on SPARK-5016:
--------------------------------------

While this doesn't solve the high dimensionality (i.e. numFeatures) problem, wouldn't distributing the k Gaussians still offer a performance gain when the number of clusters is large?

> 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
>              Labels: clustering
>
> If numFeatures or k are large, GMM EM should distribute the matrix inverse computation for Gaussian initialization.



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