We propose a generic method for iteratively approximating various second-order gradient steps--Newton, Gauss-Newton, Levenberg-Marquardt, and natural gradient--in linear time per iteration, using special curvature matrix-vector products that can be computed in O(n). Two recent acceleration techniques for on-line learning, matrix momentum and stochastic meta-descent (SMD), implement this approach. Since both were originally derived by very different routes, this offers fresh insight into their operation, resulting in further improvements to SMD.
['Nicol N. Schraudolph']
Fast curvature matrix-vector products for second-order gradient descent