Hi, I was wondering how people usually do multi-dimensional scaling on cross-validated neural distances? They are by nature unbiased and contain negative estimates because of the CV. I have a few thoughts about solutions and want to hear feedback:
- for each subject’s distance vector, add a common offset, which is the magnitude of lowest distance among all condition-wise pairs (usually the most negative one), which brings the whole vector >= zero. Then apply MDS on the median across subjects (because across subjects the distances are highly likely skewed considerably).
- just take the rank to make distance always positive.