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https://issues.apache.org/jira/browse/SPARK-14898?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15269153#comment-15269153
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Miao Wang commented on SPARK-14898:
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You are right. It currently uses eigendecomposition, val eigSym.EigSym(d, u) =
eigSym(cov.toBreeze.toDenseMatrix) // sigma = u * diag(d) * u.t. The comments
says determinant and inverse from SVD. I did not get the point of this JIRA of
using Cholesky.
> MultivariateGaussian could use Cholesky in calculateCovarianceConstants
> -----------------------------------------------------------------------
>
> Key: SPARK-14898
> URL: https://issues.apache.org/jira/browse/SPARK-14898
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: Joseph K. Bradley
> Priority: Minor
>
> In spark.ml.stat.distribution.MultivariateGaussian,
> calculateCovarianceConstants uses SVD. It might be more efficient to use
> Cholesky. We should check other numerical libraries and see if we should
> switch to Cholesky.
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