I have seen many examples of using semi-supervised learning to reduce the the number of features in a data set, but I am wondering if it is possible to somehow reduce features with purely unlabeled data.
Trivially, we can remove features that are the same value for every instance or that are very redundant in data, as these obviously cannot help us when generating a model. However, might there be some other, more non-trivial manners we may remove features from unlabeled data?
Intuition tells me that this is not possible as you can only know what a feature does/measure its worth if you can see its effect on data, but perhaps there was some caveat I have not considered. I have done some searching on here and online but all seem to include positive examples. (I have also found papers with abstracts that seem to indicate this might be possible, but the papers themselves cost $$$ which I would prefer not to spend).
Any answers/comments (even if they have links to papers) would be much appreciated, even if this is not know to be possible.