Hi Xiangrui, Thanks for the reply.
Julia code is also using the covariance matrix: (1/n)*X'*X ; Thanks, Upul On Fri, Jan 9, 2015 at 2:11 AM, Xiangrui Meng <men...@gmail.com> wrote: > The Julia code is computing the SVD of the Gram matrix. PCA should be > applied to the covariance matrix. -Xiangrui > > On Thu, Jan 8, 2015 at 8:27 AM, Upul Bandara <upulband...@gmail.com> > wrote: > > Hi All, > > > > I tried to do PCA for the Iris dataset > > [https://archive.ics.uci.edu/ml/datasets/Iris] using MLLib > > [http://spark.apache.org/docs/1.1.1/mllib-dimensionality-reduction.html > ]. > > Also, PCA was calculated in Julia using following method: > > > > Sigma = (1/numRow(X))*X'*X ; > > [U, S, V] = svd(Sigma); > > Ureduced = U(:, 1:k); > > Z = X*Ureduced; > > > > However, I'm seeing a little difference between values given by MLLib and > > the method shown above . > > > > Does anyone have any idea about this difference? > > > > Additionally, I have attached two visualizations, related to two > approaches. > > > > Thanks, > > Upul > > > > > > > > --------------------------------------------------------------------- > > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > > For additional commands, e-mail: user-h...@spark.apache.org >
