Convergence issues about picard HOT 13 CLOSED

Hi,

It's interesting, do you have a script to share? I'm especially interested in the data that you use: where are they from, what is the number of sources/ number of samples, what options do you use in picard?

Thanks :)

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The data are 64 channels x ~640 * 1001 timepoints reduced to 62 pca components due to channel interpolation. The data are centered before pca. The picard parameters are:

args = pyargs('ortho', false, 'extended', true, 'max_iter', ...
                  int32(360), 'tol', 1e-08, 'whiten', false, ...
                  'verbose', opt.verbose);

(the picard is run via matlab - python integration)
The runica was run via fieldtrip with additional options of pca, 62 and extended, true (I can check the default parameters for other options).

I can make a more rigid comparison with timings and better parameter choices (according to your suggestions). I can also share the file. The whole script is in matlab - I can share it if that would be helpful.

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with picard at least 30% of components explaining the most signal variance had almost the same strange topography (nothing physiologically meaningful) - with some components with positive and some with negative weights (meaning that the topography was very similar across these strange components but sometimes the weight signs were inverted).

from picard.

I'd be very interested in having the input matrix you give to picard ( of size 62 x 1001 if I get it right) so that I can investigate. Do you get the same behavior with whiten=True?

from picard.

I can check with whiten=True, to be precise the data is of format: 62 x 629308, how would you prefer to get the data - is dropbox link ok?

from picard.

Yes it is perfect! Thanks

from picard.

here is the link.
You can load the data using scipy.io.loadmat:

from scipy.io import loadmat

data = loadmat('data_for_ica.mat')['tmpdata']

from picard.

one note: I did centering before pca, but not after, so the channel means of the pca-transformed data are not identical to zero. However the same procedure worked well on the other files I have.

from picard.

Hi,

It converges to tol=1e-7 in about 100 iterations on my machine. I think that your problem is that you say whiten=False while the data are very far from decorrelated.

(By decorrelated I mean that np.dot(data, data.T) / data.shape[1] = Id)

from picard.

Note that when ortho=False, saying whiten=True only amounts to selecting a nice starting point for the algorithm, and does not constrain the solution.

from picard.

Ok, good to know, I thought that because I used pca I don't need to use whiten. (also because I already have one "sphering" matrix from pca and getting data from python to matlab is quite inconvenient - I preferred not to have to get sphering from picard and then combine it with pca weights).

from picard.

To get white data after pca, you only need to make sure that their std is one :) So a simple
data /= np.std(data, axis=1, keepdims=True) should do the trick.

I'm closing since it is solved.

from picard.

right, thanks!

from picard.