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ABSTRACT
We present Nonlinear Spherical Shells (NSS) as a non-iterative model-free
method for constructing approximate principal curves skeletons in volumes
of $d$ dimensional data points. NSS leverages existing model-free
techniques for nonlinear dimension to remove nonlinear artifacts in data.
With nonlinearities removed and topology preserved, data embedded by such
procedures are assumed to have properties amenable to simple
skeletonization procedures. Given these assumptions, NSS is able extract
points in the ``middle'' of the volume data and hierarchically link them
into principal curves, or a set of 1-manifolds connected at junctions.
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