In the context of dimensionality reduction one often uses word embedding, which seems to me a rather technical mathematical term, which rather stands out compared to the rest of the discussion, which in case of PCA, MDS and similar methods is just the basic linear algebra.
Yet, I would rather avoid using/interpreting this term too loosely. So, what embedding really is: the low-dimensional subspace hidden within a bigger one? The projections of the data vectors onto this subspace? The projection operator mapping the higher-dimensional space onto the lower-dimensional one, as suggested here and here? Something else?
Thank you for clarifications and examples.