[pymvpa] Justification for trial averaging?

[MS Al-Rawi]

I think a correlation classifier/method was used in Haxby's et al 2001 work, and it gave high classification accuracy using the averages. 
One might argue that, although not sure about this, assigning a volume/exemplar to a single label/condition is problematic, thus, averaging is a good option. 


Using a correlation-based classifier on exemplars/volumes would give less accuracy than the using the averages, but other powerful classifiers, e.g. SVMs, LR, ANN, RIDGE-LR will do well. 

-Rawi



> On Thursday, January 23, 2014 8:06 PM, J.A. Etzel <jetzel at artsci.wustl.edu> wrote:
> > I also agree, and will "toss in" a few more ideas:
> 
>>>  But forming decisions boundaries over features is exactly what a
>>>  classifier is meant to do, so why not just throw all these
>>>  different exemplars into the mix, and let the classifier figure out
>>>  its own notion of prototypicality?
> I think because of power, particularly the lack of it. Our datasets are
> usually massively out of balance (way more dimensions than examples),
> making learning quite difficult. It often just isn't possible to further
> subdivide the data to let the classifier learn the exemplars as well.
> 
>>>  And if you’re going to pre-classify, why pick the average
>>>  response? Why not take some kind of lower-dimensional input; the
>>>  first several eigenvectors or something, or something else?
> Probably the most common technique other than averaging is creating
> "parameter estimate images": taking the beta weights that result from
> fitting a linear model to the inputs, convolved with a hemodynamic
> response function. This can be done in programs such as SPM, and is a
> bit closer to the first level analyses done for mass-univariate analysis.
> 
>>  It seems weird to average the regressor weights, but maybe it
>>  shouldn't. Is that something that's done, or is the averaging 
> process
>>  only used with raw voxel activity?
> It does seem a bit weird, but I've done it. It feels "cleaner" to 
> generate a single set of parameter estimates than to generate one per 
> example (or run) then average, but I don't know if it is actually 
> mathematically different.
> 
> Jo
> 
> 
> -- 
> Joset A. Etzel, Ph.D.
> Research Analyst
> Cognitive Control & Psychopathology Lab
> Washington University in St. Louis
> http://mvpa.blogspot.com/
> 
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