> PCA is an orthogonal transformation of the covariance matrix

Yes you're now the second person the literally repeat the same thing I've already stated extremely clearly and succinctly: PCA is not just rotation (hint: you also need to understand covariance).

> I’m not sure why you’re both negative and dismissive. Transformation matrices in graphics are a good and approachable way to get used to linear transformations, which turn out to be useful pretty much everywhere.

I've already literally drawn the analogy/metaphor that I've drawn: if you think 2d/3d rotation matrices as they are used in graphics is any kind of introduction to the matrices in ML (modeling linear transformations or otherwise) then you're probably the type of person that believes that cash registers any kind of introduction to finance.

My point is not that hard to understand. Graphics in no way, way, shape, or form prepares you for ML. I don't understand why this is so controversial.

> My point is not that hard to understand.

Have you done any serious graphics programming? Even at the OpenGL 1.x level? What you’re saying just doesn’t make sense.

Just because you’re rotating and translating things in 3-space doesn’t negate that you have a stack of transforms that relate a point in world space to one on screen space and you want to be able to project from one to the other.

Nor does it make it any easier when you need to think about how to stack transforms to achieve effects like rendering a mirror.

I honed a lot of useful practical skill with linear algebra trying to get graphics to do what I wanted. And I say this as someone who’s spent the bulk of my career using linear algebra in the context of quantum mechanics, physical simulation, and ML-adjacent areas.