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Posted to issues@spark.apache.org by "Feynman Liang (JIRA)" <ji...@apache.org> on 2015/07/11 01:10:05 UTC
[jira] [Commented] (SPARK-6398) Improve utility of GaussianMixture
for higher dimensional data
[ https://issues.apache.org/jira/browse/SPARK-6398?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14623036#comment-14623036 ]
Feynman Liang commented on SPARK-6398:
--------------------------------------
Possibly redundant with SPARK-7210? Dimensionality's primary contribution to the method's utility is the matrix inversion while calculating the pdf in the M-step, so a better inversion method will solve this issue as well.
> Improve utility of GaussianMixture for higher dimensional data
> --------------------------------------------------------------
>
> Key: SPARK-6398
> URL: https://issues.apache.org/jira/browse/SPARK-6398
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Reporter: Travis Galoppo
> Assignee: Travis Galoppo
>
> The current EM implementation for GaussianMixture protects itself from numerical instability at the expense of utility in high dimensions. A few options exist for extending utility into higher dimensions.
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