If it is not due to C of SVM, maybe you could try smoothing before MNI normalization to see how much it would affect your results. (e.g., due to normalization and voxel oversampling).
Regards, -Rawi > On Monday, July 21, 2014 12:37 PM, Brian Murphy <[email protected]> > wrote: > > Hi Meng, > > I don't use SVMs so often, but I wonder if it is related to the setting > of the C or shrinkage parameter? With smoothing you increase the amount > of co-linearity between the input features, which can make it harder for > your algorithm to choose among features with similar informativity. > > best, > > Brian > > > > On Sun, 2014-07-20 at 17:10 +0100, Meng Liang wrote: >> Dear Jo, >> >> >> Thanks for your reply! >> >> >> I generated a series of smoothed images with Gaussian sigma from 1 mm >> to 5 mm using the same code (a for loop was used to run different >> sigma, and FSL smoothing command was used). Smoothing was done on the >> 4d nifti file directly, so I suppose it is unlikely to change the >> order of the 3d volumes. By visually inspecting the unsmoothed image >> and the smoothed image with sigma=1 mm, they look almost identical. >> The classification accuracies for all different datasets and ROIs were >> the following: >> ====================================================== >> sigma0 sigma1 sigma2 sigma3 sigma4 sigma5 >> ROI1 0.7500 0.7917 0.8333 0.8750 0.8750 0.8750 >> ROI2 0.7917 0.7917 0.7500 0.7500 0.6667 0.6667 >> ROI3 0.7917 0.7917 0.7500 0.7500 0.6250 0.5833 >> ====================================================== >> >> >> Now my impression is that it wasn't due to some mistake but smoothing >> somehow changed the distribution of the data points in the hyperspace >> in a strange way for ROI3 so that the classification accuracy was >> changed. I guess it is theorectically possible. >> >> >> If this is true, it raises another question: can we use smoothing as a >> way to test whether it is the fine-grained pattern across neiggbouring >> voxels or the very coarse pattern across different brain regions that >> drives the successful classification? The above example seems to make >> the interpretation of the results from such test a bit complicated, as >> the smoothing can have very different effect on a combined ROI (ROI3) >> than on the separate ROIs (ROI1 and ROI2). Any thoughts? >> >> >> Best, >> Meng >> >> >> >> >> >> > Date: Fri, 18 Jul 2014 16:53:54 -0500 >> > From: [email protected] >> > To: [email protected] >> > Subject: Re: [pymvpa] the effect of ROI size on classification >> accuracy >> > >> > >> > On 7/18/2014 12:06 PM, Meng Liang wrote: >> > > That's one reason I'm puzzled about the results. Having > said that, >> > > sigma=5mm smoothing equals FWHM=11.8mm smoothing, so the smoothed >> > > image does look considerably smoother than the unsmoothed image. >> > That helps - I'm more used to thinking in FWHM. 11.8 with 2x2x2 >> voxels >> > is fairly substantial and likely make some sort of difference in the >> > results. >> > >> > > I was also wondering whether this was due to some mistakes. But >> all >> > > results were generated from the same code (the only difference is >> the >> > > nifti image files being read into the script). Not sure what > other >> > > things to check... Ideas? >> > Hmm. So you have 4d niftis with the (smoothed or not) functional >> data, >> > plus 3d niftis with the ROI masks, and just send different 4d niftis >> to >> > the same classification code? I think you're right then to look at >> the >> > smoothed niftis. Perhaps something went strange with the smoothing >> > procedure, say resulting in some sort of reordering? You could try >> > something like running the images through the smoothing code, but >> with >> > zero (or nearly zero) smoothing, which shouldn't change the actual >> > functional data, to see if it turns up anything weird (i.e. if the >> > zero-smoothed images don't exactly match the before-smoothing >> images). >> > >> > Jo >> > >> > >> > -- >> > Joset A. Etzel, Ph.D. >> > Research Analyst >> > Cognitive Control & Psychopathology Lab >> > Washington University in St. Louis >> > http://mvpa.blogspot.com/ >> > >> > _______________________________________________ >> > Pkg-ExpPsy-PyMVPA mailing list >> > [email protected] >> > >> http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa >> > > -- > Dr. Brian Murphy > Lecturer (Assistant Professor) > Knowledge & Data Engineering (EEECS) > Queen's University Belfast > [email protected] > > > > _______________________________________________ > 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
