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Dimension Reduction on Polyspheres with Application to Skeletal Representations

  • Benjamin EltznerEmail author
  • Sungkyu Jung
  • Stephan Huckemann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9389)

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.

Keywords

Unit Sphere Dimension Reduction Line Element Data Space Body Organ 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Benjamin Eltzner
    • 1
    Email author
  • Sungkyu Jung
    • 2
  • Stephan Huckemann
    • 1
  1. 1.Institute for Mathematical StochasticsUniversity of GöttingenGöttingenGermany
  2. 2.Department of StatisticsUniversity of PittsburghPittsburghUSA

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