You can visualize PCA for example by val N = 2 val pc: Matrix = mat.computePrincipalComponents(N) // Principal components are stored in a local dense matrix.
// Project the rows to the linear space spanned by the top N principal components. val projected: RowMatrix = mat.multiply(pc) Each row of 'projected' now is two dimensional and can be plotted. Reza On Wed, Mar 18, 2015 at 9:14 PM, roni <roni.epi...@gmail.com> wrote: > Hi , > I am generating PCA using spark . > But I dont know how to save it to disk or visualize it. > Can some one give me some pointerspl. > Thanks > -Roni >
