Making nonlinear manifold learning models interpretable: The manifold grand tour
Smooth nonlinear topographic maps of the data distribution to guide a Grand Tour visualisation. Prioritisation of data linear views that are most consistent with data structure in the maps. Useful visualisations that cannot be obtained by other more classical approaches.
manifold learning grand tour data visualisation nonlinear dimensionality reduction linear projections