E. García Portugués, A. Meilán Vila

Skeletal representations (s-reps) have been successfully adopted to parsimoniously parametrize the shape of three-dimensional objects, and have been particularly employed in analyzing hippocampus shape variation. Within this context, we provide a fully-nonparametric dimension-reduction tool based on kernel smoothing for determining the main source of variability of hippocampus shapes parametrized by s-reps. The methodology introduces the so-called density ridges for data on the polysphere (a high-dimensional product of spheres) and involves addressing manifold computational challenges through closed formulae, efficient programming, and computational tricks.

Keywords: Density ridges, Dimension reduction, Directional data, Nonparametric Statistics, Skeletal representations.


GT09.NOPAR1 Invited Session. High dimension inference
November 7, 2023  3:30 PM
CC2: Conference Room

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