We consider the application of manifold learning ideas to the self-organization and visualization high-dimensional points residing on Grassmann and flag manifolds. To accomplish this one needs the ability to compute the distance between any two points on the manifold, and to be able to move one point in the directed of another along a geodesic. We present preliminary applications to the exploration of gene expression data, well as the visualization of hyperspectral imaging data. This is joint work with Xiaofeng Ma and Chris Peterson.