Abstract
We present a novel method that adaptively deforms a polysphere (a product of spheres) into a single high dimensional sphere which then allows for principal nested spheres (PNS) analysis. Applying our method to skeletal representations of simulated bodies as well as of data from real human hippocampi yields promising results in view of dimension reduction. Specifically in comparison to composite PNS (CPNS), our method of principal nested deformed spheres (PNDS) captures essential modes of variation by lower dimensional representations.
Cite
CITATION STYLE
Eltzner, B., Jung, S., & Huckemann, S. (2015). Dimension reduction on Polyspheres with application to skeletal representations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9389, pp. 22–29). Springer Verlag. https://doi.org/10.1007/978-3-319-25040-3_3
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