Vincent created SPARK-21049: ------------------------------- 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? -- This message was sent by Atlassian JIRA (v6.3.15#6346) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org