if you are displaying with imshow in python try with interpolation='nearest'
cheers,
satra
On Tue, Dec 13, 2011 at 3:01 PM, Jaidev Deshpande <
[email protected]> wrote:
> Hi
>
> I performed PCA on the same piece of data with MATLAB and
> sklearn.decomposition.PCA
>
> sklearn produces a much smoother 2-D plot that MATLAB, why is that?
>
> Here's what I did in MATLAB:
>
> % if x is my data
>
> >> x_cent=zscore(x) % normalizing the data
> >> [v,d]=eig(cov(x_cent))
> >> v=fliplr(v) % the eigenvectors need to be flipped because MATLAB
> returns them in the descending order
> >> x_red = x_cent' * v(:,1:2)
>
> With decomposition.PCA I did the following (it's almost like the PCA
> vs LDA example in the docs):
>
> >>> pca=PCA(n_components=2)
> >>> x_red=pca.fit(x).transform(x)
>
> What's so different about the PCA implementation here in sklearn that
> I'm getting much more streamlined points in the scatter-plot?
>
> Thanks
>
>
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