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fun, though I also got stuck on what the Y and C axes represent initially. IMO just hide the axes altogether, since the goal is just some visual clustering/similarity

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Maybe I'm slow, but clustering on what dimension? The lack of axes and labeling makes it pretty confusing to me, but I'm a dinosaur.

Visuals that are not self-explanatory make me feel dumb.

We don't know what to label those features/dimensions, because they're a reduction form higher dimensions that we also didn't bother to interrogate.

It's possible to figure them out. I wish OP would.

OP here, Is there a way to figure that out?

(Not OP) I can think of a convoluted and expensive pair-wise comparison method, but I hope there's also a way to figure this out during the application of principal component analysis in a way I don't understand.

Edit: I'm thinking it can't be done without experimentation on the embedding model.

Edit2: Ah, even that might not yield results, because as the basis is derived interstitially through computation, there's no guarantee the features of the final coordinate system will have any accessible relationship to those of the initial basis.

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