I have an issue regarding cross-validation and dimensionality reduction, specifically in the realms of neuroimaging. What I found out so far is that: if I wanted to conduct ICA on a dataset in order to extract timeseries used for prediction- it should be done strictly in the cross-validation loop.

My question now is: would it be valid to take a representative part of my dataset (given my dataset is large enough) to conduct ICA, and then use these ICA-maps on the rest of the data to extract timeseries for prediction (no leakage between the dataset used for ICA and the rest of the dataset).

A little example: say I have 200 subjects addicted to cheesecake. Now I take 50 subjects from that population and conduct ICA. Then I take the ICA maps and use them for the rest of the 150 subjects, extracting timeseries and predict labels (cross-validated). If valid, this may be save computational cost. Besides, it is probably easier to implement (although, the sample size is shrunk)

I'm looking forward to your opinions on that!


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.