[jira] [Commented] (SPARK-18581) MultivariateGaussian does not check if covariance matrix is invertible

["Sean Owen (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-18581?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15717855#comment-15717855 ] 

Sean Owen commented on SPARK-18581:
-----------------------------------

[~invkrh] do you think there's still a problem here?

> MultivariateGaussian does not check if covariance matrix is invertible
> ----------------------------------------------------------------------
>
>                 Key: SPARK-18581
>                 URL: https://issues.apache.org/jira/browse/SPARK-18581
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib
>    Affects Versions: 1.6.2, 2.0.2
>            Reporter: Hao Ren
>
> When training GaussianMixtureModel, I found some probability much larger than 1. That leads me to that fact that, the value returned by MultivariateGaussian.pdf can be 10^5, etc.
> After reviewing the code, I found that problem lies in the computation of determinant of the covariance matrix.
> The computation is simplified by using pseudo-determinant of a positive defined matrix. 
> In my case, I have a feature = 0 for all data point.
> As a result, covariance matrix is not invertible <=> det(covariance matrix) = 0 => pseudo-determinant will be very close to zero,
> Thus, log(pseudo-determinant) will be a large negative number which finally make logpdf very biger, pdf will be even bigger > 1.
> As said in comments of MultivariateGaussian.scala, 
> """
> Singular values are considered to be non-zero only if they exceed a tolerance based on machine precision.
> """
> But if a singular value is considered to be zero, means the covariance matrix is non invertible which is a contradiction to the assumption that it should be invertible.
> So we should check if there a single value is smaller than the tolerance before computing the pseudo determinant



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