Thanks for the quick response! Does something like this look appropriate?
X = np.random.randn(1000, 50) # some 50-dimensional data
p = Pipeline([ ('nystroem', Nystroem(n_components=100)), ('pca',
PCA(n_components=2)) ])
p.fit(X)
components = p.named_steps['pca'].components_
-Robert
On Thu, Apr 24, 2014 at 8:13 PM, Andreas Mueller <[email protected]> wrote:
> Yes. Just use the nystrom approximation and the apply the standard pca.
> You can also build a pipeline combining them into one step.
>
> Hth,
> Andy
> On Apr 24, 2014 7:56 PM, "Robert McGibbon" <[email protected]> wrote:
>
>> Hey All,
>>
>> Does anyone know if it's possible to use the Nystrom
>> approximation<http://scikit-learn.org/stable/modules/kernel_approximation.html#nystroem-method-for-kernel-approximation>
>> with
>> KernelPCA (e.g. [1]) in sklearn?
>>
>> -Robert
>>
>> .. [1] Improved Nystrom Low-Rank Approximation and Error Analysis
>> http://www.machinelearning.org/archive/icml2008/papers/476.pdf
>>
>>
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