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https://issues.apache.org/jira/browse/SPARK-7210?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14612291#comment-14612291
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Chris Harvey commented on SPARK-7210:
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I am new to the Apache Spark project but I would like to contribute to this
issue.
Feynman posted an R recipe for computing the pdf using a Cholesky trick. I
would like to compute the pdf by following that recipe while using the Cholesky
implementation found in Scalanlp Breeze. To test speed I would estimate the pdf
using the original method and the Cholesky method across a range of simulated
datasets with growing n and p. To test stability I would estimate the pdf on
simulated features with some multicollinearity.
Does this sound like a good starting point? Given that this is my first attempt
at contributing to an Apache project, might it be a good idea to do this
through the Mentor Programme?
> 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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