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Travis Galoppo commented on SPARK-5016: --------------------------------------- [~josephkb] Let me see what I can find; I have seen a lot of papers around the issue of high dimensional clustering when the number of samples is relatively small (so the solutions revolve around regularization, dimensionality reduction, etc)... I think here we can assume the user has a copious amount of data (why else use Spark!?!) ... I'll see what I can find. > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org