  We develop a novel analogue of Euclidean PCA (principal component analysis)
for data taking values on a Riemannian symmetric space, using totally geodesic
submanifolds as approximating lower dimnsional submanifolds. We illustrate the
technique on n-spheres, Grassmannians, n-tori and polyspheres.
