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question about the need of a perturbation term
Hi @cameronphchen sorry for bothering you. We were reading this line of code of adding a small diagonal term to the product (Line 595-597) https://github.com/brainiak/brainiak/blob/ee093597c6c11597b0a59e95b48d2118e40394a5/brainiak/funcalign/srm.py#L596
It is a bit confusing to us what is the purpose of this term. I am guessing that it serves some purpose for numerical stability. But it seems that a_subject
is not a square matrix, but rather with the same of n_voxels * n_features. Because the voxels are not necessarily ordered in a meaningful way, the diagonal matrix of 0.001 will only impact some of the voxles but not all. So I wonder whether this addition is actually necessary. Of course, it is a small term which might not impact the result in any way. But maybe the code can work without it?
Thanks!
Thanks for reaching out, Mingbo! The diagnocal matrix is indeed added for the numerical stability of the following SVD. I would expect the code to work most of the time without the diagnonal matrix. But if I recall correctly, I have also ran into situations where SVD broke.
On Mon, Jun 19, 2023 at 3:24 AM Mingbo Cai @.***> wrote:
Hi @cameronphchen https://github.com/cameronphchen sorry for bothering you. We were reading this line of code of adding a small diagonal term to the product (Line 595-597)
https://github.com/brainiak/brainiak/blob/ee093597c6c11597b0a59e95b48d2118e40394a5/brainiak/funcalign/srm.py#L596
It is a bit confusing to us what is the purpose of this term. I am guessing that it serves some purpose for numerical stability. But it seems that a_subject is not a square matrix, but rather with the same of n_voxels * n_features. Because the voxels are not necessarily ordered in a meaningful way, the diagonal matrix of 0.001 will only impact some of the voxles but not all. So I wonder whether this addition is actually necessary. Of course, it is a small term which might not impact the result in any way. But maybe the code can work without it?
Thanks!
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Thanks Cameron! I guess it is a bit unsafe to remove it given that you encountered numerical issues before. If I get a chance to try it intensively to ensure that removing this term does not cause problem, or identify the situation when SVD breaks, then I can try to make a PR removing it. Alternatively, maybe we can make it a try and except logic: by default we don't add this term, but if SVD fails, then we add the perturbation just for that iteration?