[gh0stmach1ne]

This was a good write-up but the author's claim that this is relatively unknown in machine learning is not quite accurate.

Back in grad school when we covered basic OLS methods expanding the feature space using a quadratic manifold was a common technique for teaching that regression can support nonlinear features and still remain "linear in the coefficients".

It is also implemented in widely used ML libraries. Scikit-learn, for example, has a KernelPCA object which supports using a polynomial kernel (in this instance a degree two) that computes the inner product on a explicit feature map which contains all second-order monomials.