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https://issues.apache.org/jira/browse/SPARK-21049?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-21049.
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Resolution: Invalid
Questions should go to the mailing list.
Consider what "just" computing the SVD of the original matrix entails, when
it's a huge distributed matrix. Assuming the matrix is huge but skinny, the
Gramian is small and can be handled in-core.
> why do we need computeGramianMatrix when computing SVD
> ------------------------------------------------------
>
> Key: SPARK-21049
> URL: https://issues.apache.org/jira/browse/SPARK-21049
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Affects Versions: 2.1.1
> Reporter: Vincent
>
> computeSVD will compute SVD for matrix A by computing AT*A first and svd on
> the Gramian matrix, we found that the gramian matrix computation is the hot
> spot of the overall SVD computation, but, per my understanding, we can simply
> do svd on the original matrix. The singular vector of the gramian matrix
> should be the same as the right singular vector of the original matrix A,
> while the singular value of the gramian matrix is double as that of the
> original matrix. why do we svd on the gramian matrix then?
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