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https://issues.apache.org/jira/browse/SPARK-7210?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14609040#comment-14609040
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Feynman Liang commented on SPARK-7210:
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[~josephkb] can you clarify whether this issue is for computing covariance
inverse (which is currently private in MultivariateGaussian), or for computing
the Multivariate Gaussian PDF (potentially skipping the intermediate
representation as a precision matrix)?
[~shivaram] do you mind explaining why QR => SVD would be more stable? I am not
too familiar with this. Thanks.
> Test matrix decompositions for speed vs. numerical stability for Gaussians
> --------------------------------------------------------------------------
>
> Key: SPARK-7210
> URL: https://issues.apache.org/jira/browse/SPARK-7210
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Reporter: Joseph K. Bradley
> Priority: Minor
>
> We currently use SVD for inverting the Gaussian's covariance matrix and
> computing the determinant. SVD is numerically stable but slow. We could
> experiment with Cholesky, etc. to figure out a better option, or a better
> option for certain settings.
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