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Refining Invariant Coordinate Selection via Local Projection Pursuit

In: Robust and Multivariate Statistical Methods

Author

Listed:
  • Lutz Dümbgen

    (University of Bern)

  • Katrin Gysel

    (SAKK)

  • Fabrice Perler

    (Bundesamt für Gesundheit)

Abstract

Invariant coordinate selection (ICS), introduced by Tyler et al. (J. Roy. Stat. Soc. B 71(3):549–592, 2009), is a powerful tool to find potentially interesting projections of multivariate data. In some cases, some of the projections proposed by ICS come close to really interesting ones, but little deviations can result in a blurred view which does not reveal the feature (e.g., a clustering), which would otherwise be clearly visible. To remedy this problem, we propose an automated and localized version of projection pursuit (PP), cf. Huber (Ann. Stat. 13(2):435–525, 1985). Precisely, our local search is based on gradient descent applied to estimated differential entropy as a function of the projection matrix.

Suggested Citation

  • Lutz Dümbgen & Katrin Gysel & Fabrice Perler, 2023. "Refining Invariant Coordinate Selection via Local Projection Pursuit," Springer Books, in: Mengxi Yi & Klaus Nordhausen (ed.), Robust and Multivariate Statistical Methods, pages 121-136, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-22687-8_6
    DOI: 10.1007/978-3-031-22687-8_6
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