[pymvpa] one class again

[basile pinsard]

Hi Nick,

many thanks for your answers.
It seems that not many are trying one-class problems.

I tried Pymvpa(libsvm) and scikit-learn OCSVMs but both doesn't scale well.

Also tried LDA but there was a bug when using one-target dataset for
training, and made a fix that I'm gonna push.
However when predicting, the classifier can only assign to the class which
has been trained, which is expected as it doesn't know else, but I can
still use the ca.estimates values.

Thanks.

basile


On Sun, Feb 22, 2015 at 8:02 AM, Nick Oosterhof <
n.n.oosterhof at googlemail.com> wrote:

>
> On 20 Feb 2015, at 22:24, basile pinsard <basile.pinsard at gmail.com> wrote:
>
> >
> > I have questions regarding the one-class svm implementation:
> > SVM(svm_impl='ONE_CLASS')
> > when training on a dataset of small size (where there might not be any
> structure) the call to train often hangs forever.
>
> It is possible that libSVM takes a very long time to converge [1].
>
> > When I create a random dataset with the same dimensions, or if I replace
> the content of the sample array with random, it runs smoothly.
> > I checked in my dataset and there are no nans or infs.
> > Could this be just the distribution of the data that causes this to hang?
>
> Yes, if there is very little structure in the data, this could happen. You
> could use another classifier (e.g. LDA).
>
> Alternatively, it may be possible to change some of the parameters used
> for SVM (I have very little experience with this).
>
> >
> > I know that this problem is due to LibSVM but I wanted to confirm that
> it is the last version that is included in PyMVPA.
>
> The version in included in PyMVPA seems to be 2.89 [2], but that is not
> the latest one. Currently the latest version is 3.20 [3].
>
> [1] http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#f412
> [2] https://github.com/PyMVPA/PyMVPA/tree/master/3rd/libsvm
> [3] http://www.csie.ntu.edu.tw/~cjlin/libsvm/
>
>
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-- 
Basile Pinsard

*PhD candidate, *
Laboratoire d'Imagerie Biomédicale, UMR S 1146 / UMR 7371, Sorbonne
Universités, UPMC, INSERM, CNRS
*Brain-Cognition-Behaviour Doctoral School **, *ED3C*, *UPMC, Sorbonne
Universités
Biomedical Sciences Doctoral School, Faculty of Medicine, Université de
Montréal
CRIUGM, Université de Montréal
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