Hi all, I'm trying to figure out a problem about HyperAlignment. As explained HyperAlignment tries to align brain trajectories in a common representation space. Now I'd like to use in combination hyperalignment and cross decoding, thus training a classifier with hyperaligned data and then use it with other (hyperaligned) data. The main issue is that using the manual cross validation, as in the example, I will have n_fold hyperalignment functions and n_fold cross decoding predictions while I would like to have an hyperalignment function and a single list of predictions. Using hyperalignment with full dataset lead to circularity because I need also to estimate hyperalignment classification accuracy.
Is there a way to cross validate hyperalignment parameter as for classification tasks? Or the question is theoretically impossible? Thank you Roberto
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