[pymvpa] Hyperalignment: SVD did not converge

Kiefer Katovich kieferk at stanford.edu
Tue May 1 18:38:33 UTC 2012

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,


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