Thibault Pautrel

Riemannian stochastic optimization for sufficient dimension reduction

Gradient-based sufficient dimension reduction methods face a fundamental tradeoff: computing local gradients in the ambient space yields closed-form solutions but suffers from the curse of dimensionality, while iterative refinement in the projected space improves statistical efficiency at $O(n^2 p)$…