Earlier quoted context omitted.
SVD is the decomposition of a matrix into two rotation matrices and a scaling matrix, by definition: https://en.wikipedia.org/wiki/Singular_value_decomposition
i don't understand who is having trouble reading the dialogue here you or i; > there is absolutely no sense in which the SVD/PCA decomposition is just a rotation matrix... (hint: scaling is extremely important) ... > SVD is the decomposition of a matrix into two rotation matrices and a scaling matrix, by definition: yes that's exactly what i was implying when i said SVD more than just rotation, scaling is also import…
> I've been in ML for ~5 years in multiple FAANGs and I have never seen a rotation matrix.
Presumably you've used SVD, but you've never seen a rotation matrix. So something is cooked.
Maybe corollary: that FAANG job wasn't that interesting.