When I was working on a recommender for television shows, I ran SVD on a large User/Item matrix to create a low rank approximation, essentially reducing thousands of user features (TV show preferences) to user vectors representing twenty or thirty abstract "features". Then I looked at the actual item preferences of users who expressed each feature at the greatest and least magnitude. The features, in some cases, mapped to recognizable constructs. There were distinct masculine and feminine features, several obvious Hispanic / Latino elements, and strong liberal versus conservative indicators. Others were less explainable using common labels.

It struck me at the time that the qualities that were expressed most strongly were the ones that ended up having names in our language. But there were others for which I would say to myself, there is something about this group (e.g. those with the greatest expressed value of F124) that I recognize, but can't quite put my finger on.

Of course, I was looking at people through a keyhole, their TV viewing preferences being the only information I had.

Also, I noticed that these "came into focus" most clearly at a certain level of compression (rank).

FWIW