[pymvpa] Hyperalignment: SVD did not converge

Sat May 19 19:21:30 UTC 2012

I could definitely use an ROI for the feature selection, but preferably I
would be able to do a feature selection using the whole brain without any a
priori assumptions about which areas will contain the best voxels. Is there
a more appropriate feature selector than the one way anova that could
potentially give me a better set of voxels from the whole brain to use in
hyperalignment?

On Sat, May 19, 2012 at 12:04 PM, Swaroop Guntupalli <swaroopgj at gmail.com>wrote:

> Looks like it could be a problem with the voxels selected. It is possible
> that the voxel selection one way vs the other is leading to degenerate
> matrices for SVD to work on. If you have an independent ROI
> (function/anatomical), try hyperalignment on voxels within that ROI.
>
> Swaroop
>
>
>
>
> On Fri, May 18, 2012 at 7:27 PM, Kiefer Katovich <kieferk at stanford.edu>wrote:
>
>> Hi again,
>>
>> I will try those two ways of outputting the feature selection, they both
>> seem straightforward.
>>
>> I spoke to soon on the SVD non-convergence issue. As it turns out it
>> really seems to depend on how I set the targets for the datasets. SVD seems
>> to converge better when there are more "classes" set with the targets. For
>> example, if i set there to be 8 different categories in my data SVD can
>> converge on the entire dataset, but if I set it to be binary it has a lot
>> of trouble converging.
>>
>> For example, I set the first two TRs of every trial to 1 and all other
>> TRs to 0 in the targets file. If I use this for feature selection and then
>> hyperalignment, it does not converge with most combinations of subjects.
>>
>> However, if I designate trialtype and specific TRs within the targets
>> file it is able to converge with all the subjects.
>>
>> I'm not to clear on why this would be the case. I assume that the anova
>> feature selection is picking out the voxels that best match the time series
>> of the targets that you assign to each TR? I know that in a univariate
>> solution with the binary targets there are definitely voxels that correlate
>> significantly with the 1s (first two TRs of every trial), so I figured that
>> the feature selector would probably pull out those.
>>
>> I hope that wasn't too confusing. I'm just wondering if there is some
>> criterion that I am missing when assigning the target file that is
>> necessary for hyperalignment to run correctly.
>>
>> Thank you,
>> Kiefer
>>
>>
>>
>> On Fri, May 18, 2012 at 12:29 PM, Swaroop Guntupalli <swaroopgj at gmail.com
>> > wrote:
>>
>>> Hi Kiefer,
>>>
>>> Glad that it's working.
>>> Whatever mapper you are using (StaticFeatureSelection?) should have a
>>> slicearg argument that contains the list of voxel indices.
>>> Another way is to create an array of ones of the same size as the number
>>> of features selected and pass it bakward through the mappers through which
>>> the Data came through before hyperalignment
>>> (mapper_name.reverse(new_data)), which should put the data in the original
>>> space. You can then use map2nifti to map those selected voxels (as ones)
>>> into a nifti file.
>>> Does that make sense?
>>>
>>> Best,
>>> Swaroop
>>>
>>>
>>>
>>>
>>> On Fri, May 18, 2012 at 3:02 PM, Kiefer Katovich <kieferk at stanford.edu>wrote:
>>>
>>>> Hey Swaroop,
>>>>
>>>> I actually managed to fix the SVD non-convergence issue. Turns out that
>>>> I had foolishly not been lagging my data for the hemodynamic response. Once
>>>> I lagged the targets appropriately, i was able to hyperalign all of the
>>>> brains without encountering any SVD problems.
>>>>
>>>> I would like to visualize the features that are being selected that
>>>> hyperalignment is using for the transformation. What should I do after
>>>> preforming the OneWayAnova and the StaticFeatureSelector to save those
>>>> selected features into a nifti that I can overlay on the subjects' brains?
>>>> It would be really nice to know which areas of the brain end up being
>>>> selected for alignment (I am allowing it to choose the top 5% voxels of any
>>>> voxels in the brain).
>>>>
>>>> Thanks for your help!
>>>> Kiefer
>>>>
>>>>
>>>>
>>>> On Fri, May 18, 2012 at 6:33 AM, Swaroop Guntupalli <
>>>> swaroopgj at gmail.com> wrote:
>>>>
>>>>> Hi Kiefer,
>>>>>
>>>>> Sorry for late response (I blame abstract submission deadlines).
>>>>>
>>>>> I sometimes (very few though) encounter this SVD non convergence
>>>>> problem.
>>>>> One workaround I use (and that works for me) is to try a different SVD
>>>>> implementation: dgesvd instead of numpy (option in ProcrusteanMapper)
>>>>> If it doesn't work any SVD implementation, it means the matrix is
>>>>> probably bad for some reason, which might mean one or more of the data
>>>>> matrices is messed up (SVD is on the product of 2 data matrices), so
>>>>> make sure you exclude all invariant voxels from the data (you can do that
>>>>> using "remove_invariant_features".
>>>>> HTH.
>>>>> Keep us posted on your progress.
>>>>>
>>>>> Thanks,
>>>>> Swaroop
>>>>>
>>>>>
>>>>>
>>>>> On Tue, May 1, 2012 at 2:38 PM, Kiefer Katovich <kieferk at stanford.edu>wrote:
>>>>>
>>>>>> Hi again,
>>>>>>
>>>>>> Sorry my messages keep starting new threads; I've been receiving email
>>>>>> in digest mode but changed my settings to single mail, so I should be
>>>>>> able to reply properly soon.
>>>>>>
>>>>>> First off – I re-ran the iterative test of hyperalignment starting
>>>>>> with a different set of subjects. This time 8 of the 21 subjects
>>>>>> managed to be hyperaligned to each other, and most of the successful
>>>>>> subjects were different than in the last batch. I just did this to
>>>>>> confirm that the success of hyperalignment is contingent upon the
>>>>>> unique set of datasets that you put into it, and not just that some
>>>>>> subjects were bad and others good.
>>>>>>
>>>>>> Now, on to your comments:
>>>>>>
>>>>>> Thanks for the clarification on hyperalignment and SVD. I should
>>>>>> probably read the source code to get a better idea of exactly what
>>>>>> hyperalignment and the procrustean transformation is attempting to do
>>>>>> with the datasets I give it.
>>>>>>
>>>>>> By "classification error" I only meant the way in which I had coded
>>>>>> the time points of the dataset into separate classes, not actually
>>>>>> running a classification algorithm. Sorry for the misconception, that
>>>>>> was poor phrasing on my part.
>>>>>>
>>>>>> A related question: how much of an impact does the coding of time
>>>>>> points have on hyperalignment? I assume that the feature selector,
>>>>>> such as OneWayAnova, chooses features according to the "targets" that
>>>>>> you assign to each time point, and that this is then fed into
>>>>>> hyperalignment and procrustean?
>>>>>>
>>>>>> Here are some details on my data:
>>>>>>
>>>>>> 432 time points
>>>>>> ~58000 voxels per time point (whole brain, masked)
>>>>>> 3000 features selected using FixedNElementTailSelector
>>>>>>
>>>>>> I assumed that it is only the 3000 features from the tail selector
>>>>>> that hyperalignment and procrustean use to make the alignment?
>>>>>>
>>>>>> Ideally I would not have to mask out to a specific area of the brain
>>>>>> prior to the feature selection. I prefer, for this data, to not make
>>>>>> an initial assumption about which brain areas contain the best
>>>>>> features for alignment.
>>>>>>
>>>>>> Thanks you,
>>>>>>
>>>>>> Kiefer
>>>>>>
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