(Oh sorry, I've only been thinking of TallSkinnySVD) On Tue, Mar 24, 2015 at 6:36 PM, Reza Zadeh <r...@databricks.com> wrote: > If you want to do a nonstandard (or uncentered) PCA, you can call > "computeSVD" on RowMatrix, and look at the resulting 'V' Matrix. > > That should match the output of the other two systems. > > Reza > > On Tue, Mar 24, 2015 at 3:53 AM, Sean Owen <so...@cloudera.com> wrote: >> >> Those implementations are computing an SVD of the input matrix >> directly, and while you generally need the columns to have mean 0, you >> can turn that off with the options you cite. >> >> I don't think this is possible in the MLlib implementation, since it >> is computing the principal components by computing eigenvectors of the >> covariance matrix. The means inherently don't matter either way in >> this computation. >> >> On Tue, Mar 24, 2015 at 6:13 AM, roni <roni.epi...@gmail.com> wrote: >> > I am trying to compute PCA using computePrincipalComponents. >> > I also computed PCA using h2o in R and R's prcomp. The answers I get >> > from >> > H2o and R's prComp (non h2o) is same when I set the options for H2o as >> > standardized=FALSE and for r's prcomp as center = false. >> > >> > How do I make sure that the settings for MLib PCA is same as I am using >> > for >> > H2o or prcomp. >> > >> > Thanks >> > Roni >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >> For additional commands, e-mail: user-h...@spark.apache.org >> >
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