riemannian-geometry

This paper introduces a Riemannian version of archetypal analysis using data-driven pullback geometry to combine interpretability with non-linear expressiveness, proposing the Riemannian Archetypal Mapping (RAM) and demonstrating its effectiveness on synthetic data and MNIST.