• Corpus ID: 239050313

Hyperspherical Dirac Mixture Reapproximation

  title={Hyperspherical Dirac Mixture Reapproximation},
  author={Kailai Li and Florian Pfaff and Uwe D. Hanebeck},
  • Kailai Li, F. Pfaff, U. Hanebeck
  • Published 20 October 2021
  • Computer Science, Mathematics, Engineering
  • ArXiv
We propose a novel scheme for efficient Dirac mixture modeling of distributions on unit hyperspheres. A so-called hyperspherical localized cumulative distribution (HLCD) is introduced as a local and smooth characterization of the underlying continuous density in hyperspherical domains. Based on HLCD, a manifold-adapted modification of the Cramér–von Mises distance (HCvMD) is established to measure the statistical divergence between two Dirac mixtures of arbitrary dimensions. Given a (source… 

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  • Kailai Li, F. Pfaff, U. Hanebeck
  • Computer Science
    2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
  • 2020
A novel deterministic sampling approach for von Mises–Fisher distributions of arbitrary dimensions where samples of configurable size are drawn isotropically on the hypersphere while preserving the mean resultant vector of the underlying distribution.