Thank you, Gaël. Indeed memory is a problem if X is transformed to dense
one. I then tried with a mini set with only about 1000 features.
Your suggestion does solve the error for Isomap.
However, for LDA and SparsePCA, I am getting a same error: "ValueError -
matrices are not aligned." For both, the problem traces to defmatrix.py in
numpy. I get confused again...
On 26 October 2011 15:19, Gael Varoquaux <[email protected]>wrote:
> On Wed, Oct 26, 2011 at 03:02:28PM +0200, SK Sn wrote:
> > Hi there, I am trying to apply and test several dimension reduction
> methods
> > on 20Newsgroup data. However, I got errors, which I did not get how, on
> all
> > of them except RandomPCA. Would you please help me to get a better
> > understand of the issue?
>
> > X = Vectorizer(max_features=10000).fit_transform(data_set.data)
>
> I think that your problem is that X (returned by the Vectorizer) is a
> sparse matrix, and that the different methods other than the
> RandomizedPCA do not accept sparse matrices as inputs.
>
> You can make the data dense using
> X = X.todense()
>
> This will consume much more memory, and might not be an option, though.
>
> HTH,
>
> Gaël
>
>
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