Could you compare V directly and tell us more about the difference you
saw? The column of V should be the same subject to signs. For example,
the first column of V could be either [0.8, -0.6, 0.0] or [-0.8, 0.6,
0.0]. -Xiangrui
On Sat, Jan 10, 2015 at 8:08 PM, Upul Bandara upulband...@gmail.com
Hi Xiangrui,
Thanks a lot for you answer.
So I fixed my Julia code, also calculated PCA using R as well.
R Code:
-
data - read.csv('/home/upul/Desktop/iris.csv');
X - data[,1:4]
pca - prcomp(X, center = TRUE, scale=FALSE)
transformed - predict(pca, newdata = X)
Julia Code (Fixed)
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.
You need to subtract mean values to obtain the covariance matrix
(http://en.wikipedia.org/wiki/Covariance_matrix).
On Fri, Jan 9, 2015 at 6:41 PM, Upul Bandara upulband...@gmail.com wrote:
Hi Xiangrui,
Thanks for the reply.
Julia code is also using the covariance matrix:
(1/n)*X'*X ;
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
