Vincent created SPARK-21049:
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Summary: 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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