[pymvpa] Use searchlight algorithm only for predictions.

[MS Al-Rawi]

It is indeed a concept question. Maybe you could try recurrent neural networks cause they deal with arbitrary input sequences instead of static input data only (eg, same input size) . Beware that RNNs are used (frequently) to consider time varying data, I am not sure if they can be used with occluded, or whole versus sub-data. 

Cheers,
-Rawi









>________________________________
> From: Roberto Guidotti <robbenson18 at gmail.com>
>To: Development and support of PyMVPA <pkg-exppsy-pymvpa at lists.alioth.debian.org> 
>Sent: Thursday, January 31, 2013 2:53 PM
>Subject: Re: [pymvpa] Use searchlight algorithm only for predictions.
> 
>
>Thank you guys,
>
>
>Yes, I know that I can't predict using a portion of voxel. Let's say that I would like to train on full brain and test on a portion, putting out of ROI voxel intensity to zero. 
>I don't know if it makes sense conceptually because I would like to predict using a portion of features on a model built on multiple features. 
>
>
>Probably could be an sensitivity measure, e.g. building a classifier to predict from flag the country for example Liberarian flag and stars and stripes, If I use features from stripes part of the flag (common in both of the flags) the classifier isn't able to classify - well, using a feature selection probably those features were discarded - but using "stars" part as ROI the classifier identifies the flag, and so I will know where the classifier is more sensitive! (Hope I explained it clear).
>
>
>I don't know if there is still a theoretical problem.
>
>
>Thank you
>R
>
>
>PS: If I could help you to complete...
>
>On 31 January 2013 15:38, Yaroslav Halchenko <debian at onerussian.com> wrote:
>
>
>>On Thu, 31 Jan 2013, Michael Hanke wrote:
>>>      As the subject "clearly" says, I would like to train a classifier using
>>>      classical methods for example using all voxels and then try to predict
>>>      using only a portion of ROI like a searchlight.
>>>      I've tried to do this using the classifier as data measure in
>>>      searchlight class, but obviously the features of the classifier are more
>>>      than those used in the searchlight.
>>>      How can I do?
>>
>>>    This is less of a technical question, but more of a conceptual one. You
>>>    can't train an algorithm on one set of features and then run it on a
>>>    different one with a different number of features.
>>>    you need to have equally structured input in both training and testing
>>>    stage. This could be done (think e.g. PCA projection), but whether it
>>>    makes sense in you context is impossible to tell at this point.
>>
>>indeed!   But I guess it could be stretched to become a "technically
>>legit" one in the case of kernel-based classifiers, where optimization
>>and decision is done based on values within the kernel...  theoretically
>>it should be possible to get the solution for one kernel (estimated on
>>full data) and then apply to another (estimated on subset of the
>>features)... not sure how legit it would be, but at least possible
>>technically --  I guess could become an improved "sensitivity"
>>measure to complement existing ones ;-)
>>
>>--
>>Yaroslav O. Halchenko
>>Postdoctoral Fellow,   Department of Psychological and Brain Sciences
>>Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
>>Phone: +1 (603) 646-9834                       Fax: +1 (603) 646-1419
>>WWW:   http://www.linkedin.com/in/yarik
>>
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