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Manoj Kumar edited comment on SPARK-5016 at 2/5/15 8:09 AM: ------------------------------------------------------------ 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. -- 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