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Central axes and peripheral points in high dimensional directional datasets

Author

Listed:
  • Annabella Astorino

    (Università delle Calabria)

  • Manlio Gaudioso

    (Università delle Calabria)

  • Alberto Seeger

    (University of Avignon)

Abstract

We introduce a new notion of central axis for a finite set $$\{a_1,\ldots ,a_m\}$$ { a 1 , … , a m } of vectors in $$\mathbb {R}^n$$ R n . In tandem, we discuss different ways of measuring the dispersion of the data points $$a_i$$ a i ’s around the central axis. Finally, we explain how to detect numerically the most peripheral points of the given dataset.

Suggested Citation

  • Annabella Astorino & Manlio Gaudioso & Alberto Seeger, 2016. "Central axes and peripheral points in high dimensional directional datasets," Computational Optimization and Applications, Springer, vol. 65(2), pages 313-338, November.
    • Handle: RePEc:spr:coopap:v:65:y:2016:i:2:d:10.1007_s10589-014-9724-2
    DOI: 10.1007/s10589-014-9724-2
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    References listed on IDEAS

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    1. J. L. Goffin, 1980. "The Relaxation Method for Solving Systems of Linear Inequalities," Mathematics of Operations Research, INFORMS, vol. 5(3), pages 388-414, August.
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