Is there a name for approximating the Hessian as the outer product of the gradient with itself?
If one is approximating the Hessian of the log-loss, then the outer product of the gradient with itself is the Fisher information matrix. What about in general?
I'm looking at this in the context of explaining what the so-called Gauss-Newton matrix assumes (Schraudolph, N. N. (2002). Fast curvature matrix-vector products for second-order gradient descent. Neural Computation, 14(7), 1723–38.). We have an input vector, followed by a linear transformation, followed by a nonlinear loss function. The Hessian of the linear transformation (A) is approximated as an outer-product of gradients. The Hessian of the negative log-loss function (B) is assumed to be positive semidefinite. My question is what is the assumption A called?