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 <[email protected]> >To: Development and support of PyMVPA ><[email protected]> >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 <[email protected]> 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 >> >>_______________________________________________ >>Pkg-ExpPsy-PyMVPA mailing list >>[email protected] >>http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa >> > >_______________________________________________ >Pkg-ExpPsy-PyMVPA mailing list >[email protected] >http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa > > _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
