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
