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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2015/04/28 23:20:07 UTC

[jira] [Created] (SPARK-7210) Test matrix decompositions for speed vs. numerical stability for Gaussians

Joseph K. Bradley created SPARK-7210:
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             Summary: 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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