Hi Kathy.
Why do you want to do that? The Chi2 Kernel is only defined for
non-negative data, so it makes sense that the approximation only works with
non-negative data. The Chi2 Kernel is mostly used for histogram data.
Cheers,
Andy
On 10/14/2014 09:13 PM, Kathy Hida wrote:
In Scikit-learn
Hi Luca,
The other part of the decomposition that you're missing is available
in `spca.components_` and has shape `(n_components, n_features)`. The
approximation of X is therefore `np.dot(x_3_dimensional,
spca.components_)`.
Best,
Vlad
On Thu, Oct 16, 2014 at 6:07 PM, Luca Puggini wrote:
> Hi,
Hi,
is there any way to reconstruct the data after SparsePCA?
If I do
spca = SparsePCA(alpha=1, n_components=3).fit(x)
x_3_dimensional = SparsePCA.transform(x)
How can I get the best lower rank approximation of x after SparsePCA
decomposition?
Thanks,
Luca
--
Have a look at the content of
`adabost_classifier_model.estimators_` after you call fit on it.
--
Olivier
--
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