[pymvpa] Use searchlight algorithm only for predictions.
rawi707 at yahoo.com
Thu Jan 31 15:34:21 UTC 2013
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.
> 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.
>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
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