In its current implementation, the principal components are computed in MLlib in two steps: 1) In a distributed fashion, compute the covariance matrix - the result is a local matrix. 2) On this local matrix, compute the SVD.
The sorting comes from the SVD. If you want to get the eigenvalues out, you can simply run step 1 yourself on your RowMatrix via the (experimental) computeCovariance() method, and then run SVD on the result using a library like breeze. - Evan On Tue, Sep 23, 2014 at 12:49 PM, st553 <sthompson...@gmail.com> wrote: > sowen wrote > > it seems that the singular values from the SVD aren't returned, so I > don't > > know that you can access this directly > > Its not clear to me why these aren't returned? The S matrix would be useful > to determine a reasonable value for K. > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/spark1-0-principal-component-analysis-tp9249p14919.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > >