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https://issues.apache.org/jira/browse/SPARK-14898?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15268548#comment-15268548
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Sean Owen commented on SPARK-14898:
-----------------------------------
Does this use the SVD currently? it looks like it just needs an
eigendecomposition and uses a special-purpose routine for that. We don't need
to use the SVD to get eigenvalues; I actually don't know how to get eigenvalues
from a Cholesky decomposition, but could be forgetting my linear algebra. But
no the idea is not to use the SVD to get a Cholesky decomposition. If you did
that you'd be done already, and it's overkill.
> 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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