On Thu, Jul 31, 2014 at 7:31 PM, Olivier Grisel
<[email protected]> wrote:
> The sign of the components is not deterministic. The absolute values
> should be the same.
But the last component differs even in absolute values,
Pca_components:
[[-0.61098855 0.54326104 0.3089728 0.29598692 -0.01767037 0.25638981
-0.24499776 -0.02891609 -0.10832649 0.09922362]
[-0.04285247 0.58774877 -0.02102497 -0.18987243 -0.09547488 -0.37323125
0.62203739 0.03558049 0.0780125 -0.27114941]
[ 0.3481033 0.32602361 -0.20594773 -0.26088651 -0.63592937 0.39839983
-0.20642102 -0.23287936 -0.0447964 0.00887286]]
SVD on X (matches pca since it follows the same steps)
Below is V from svd of (X)
[[-0.61098855 0.54326104 0.3089728 0.29598692 -0.01767037 0.25638981
-0.24499776 -0.02891609 -0.10832649 0.09922362]
[-0.04285247 0.58774877 -0.02102497 -0.18987243 -0.09547488 -0.37323125
0.62203739 0.03558049 0.0780125 -0.27114941]
[ 0.3481033 0.32602361 -0.20594773 -0.26088651 -0.63592937 0.39839983
-0.20642102 -0.23287936 -0.0447964 0.00887286]
SVD on X.T:
Below is U.T of SVD(X.T)
[-0.61098855 0.54326104 0.3089728 0.29598692 -0.01767037 0.25638981
-0.24499776 -0.02891609 -0.10832649 0.09922362]
[-0.04285247 0.58774877 -0.02102497 -0.18987243 -0.09547488 -0.37323125
0.62203739 0.03558049 0.0780125 -0.27114941]
[ 0.39253032 0.27426443 -0.09946156 -0.17669364 -0.55699776 0.60982874
0.0461356 -0.18837449 -0.04298647 0.08936917]
You can see that the final component doesn't match in terms of absolute values.
I am pasting the code I used for svd(X.T) to check if I am making some
mistakes here.
def svd_t(X):
#x.shape = n_features,n_samples
#every sample is a column
mean= np.mean(X,axis=1)
mean = mean[:,np.newaxis]
X = X -mean
u,s,v = np.linalg.svd(X,full_matrices=True)
return u,s,v
Thanks
Deepak
--
With Regards,
Deepak Pandian
"Deconstructing world one piece at a time"
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