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
