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Weak Similarities of Finite Ultrametric and Semimetric Spaces

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Abstract

Weak similarities form a special class of mappings between semimetric spaces. Two semimetric spaces X and Y are weakly similar if there exists a weak similarity Φ: XY. We find a structural characteristic of finite ultrametric spaces for which the isomorphism of its representing trees implies a weak similarity of the spaces. We also find conditions under which the Hasse diagrams of balleans of finite semimetric spaces are isomorphic.

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References

  1. O. Dovgoshey and E. Petrov, “Weak similarities of metric and semimetric spaces,” Acta Math. Hungar. 141 (4), 301–319 (2013).

    Article  MathSciNet  MATH  Google Scholar 

  2. O. Dovgoshey, E. Petrov, and H.-M. Teichert, “How rigid the finite ultrametric spaces can be?,” J. Fixed Point Theory Appl. 19 (2), 1083–1102 (2017).

    Article  MathSciNet  MATH  Google Scholar 

  3. S. Agarwal, J. Wills, L. Cayton, G. Lanckriet, D. Kriegman, and S. Belongie, “Generalized non-metric multidimensional scaling,” Proceedings of the Eleventh Intern. Conference on Artificial Intelligence and Statistics, pp. 11–18 (PMLR, San Juan, Puerto Rico, 2007).

    Google Scholar 

  4. I. Borg and P. Groenen, Modern Multidimensional Scaling: Theory and Applications (Springer, New York, 2005).

    MATH  Google Scholar 

  5. K. Jamieson and R. Nowak, “Active ranking using pairwise comparisons,” Proceedings of the 24th Intern. Conference on Neural Information Processing Systems, pp. 2240–2248 (Curran Associates Inc., USA, 2011).

    Google Scholar 

  6. M. Kleindessner and U. von Luxburg, “Uniqueness of ordinal embedding,” Proceedings of the 27-th Conference on Learning Theory, pp. 40–67 (PMLR, Barcelona, Spain, 2014).

    Google Scholar 

  7. J. B. Kruskal, “Nonmetric multidimensional scaling: A numerical method,” Psychometrika 29 (2), 115–129 (1964).

    Article  MathSciNet  MATH  Google Scholar 

  8. M. Quist and G. Yona, “Distributional scaling: An algorithm for structure-preserving embedding of metric and nonmetric spaces,” J. Mach. Learn. Res. 5, 399–420 (2004).

    MathSciNet  MATH  Google Scholar 

  9. R. Rosales and G. Fung, “Learning sparse metrics via linear programming,” Proceedings of the 12th ACM SIGKDD Intern. Conference on Knowledge Discovery and Data Mining, pp. 367–373 (ACM, New York, 2006).

    Chapter  Google Scholar 

  10. R. Shepard, “The analysis of proximities: multidimensional scaling with an unknown distance function. II,” Psychometrika 27 (3), 219–246 (1962).

    Article  MathSciNet  MATH  Google Scholar 

  11. R. Shepard, “Metric structures in ordinal data,” J. Math. Psychology 3 (2), 287–315 (1966).

    Article  Google Scholar 

  12. F. Wauthier, M. Jordan, and N. Jojic, “Efficient ranking from pairwise comparisons,” Proceedings of the 30th Intern, Conference onMachine Learning, pp. 109–117 (PMLR, Atlanta, Georgia, USA, 2013).

    Google Scholar 

  13. L.M. Blumenthal, Theory and Applications of Distance Geometry (Oxford, Clarendon Press, 1953).

    MATH  Google Scholar 

  14. J. A. Bondy and U. S.R. Murty, Graph theory, Graduate Texts in Mathematics 244 (Springer, New York, 2008).

    Book  MATH  Google Scholar 

  15. R. Diestel, Graph Theory, Graduate Texts in Mathematics 173 (Springer, Berlin, 2005).

    MATH  Google Scholar 

  16. D. Dordovskyi, O. Dovgoshey, and E. Petrov, “Diameter and diametrical pairs of points in ultrametric spaces,” p-adic Numbers Ultrametric Anal. Appl. 3 (4), 253–262 (2011).

    Article  MathSciNet  MATH  Google Scholar 

  17. E. Petrov and A. Dovgoshey, “On the Gomory-Hu inequality,” J. Math. Sci., New York 198 (4), 392–411 (2014). Translation from Ukr.Mat. Visn. 10 (4), 469–496 (2013).

    Article  MathSciNet  MATH  Google Scholar 

  18. M. Vyalyi and V. Gurvich “Ultrametrics, trees, flows and bottleneck arcs,” Mat. Pros. 16, 75–88 (2012) [in Russian].

    Google Scholar 

  19. R. I. Grigorchuk, V.V. Nekrashevich, and V. I. Sushanskyi, “Automata, dynamical systems, and groups,” Proc. V.A. Steklov Inst.Math. 231, 128–203 (2000).

    MathSciNet  MATH  Google Scholar 

  20. V. Gurvich and M. Vyalyi, “Characterizing (quasi-)ultrametric finite spaces in terms of (directed) graphs,” Discrete Appl.Math. 160 (12), 1742–1756 (2012).

    Article  MathSciNet  MATH  Google Scholar 

  21. B. Hughes, “Trees and ultrametric spaces: a categorical equivalence,” Adv. Math. 189 (1), 148–191 (2004).

    Article  MathSciNet  MATH  Google Scholar 

  22. E. A. Petrov, “Ball-preserving mappings of finite ulrametric spaces,” Proceedings of IAMM 26, 150–158 (2013) [in Russian].

    Google Scholar 

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Correspondence to Evgeniy Petrov.

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Petrov, E. Weak Similarities of Finite Ultrametric and Semimetric Spaces. P-Adic Num Ultrametr Anal Appl 10, 108–117 (2018). https://doi.org/10.1134/S2070046618020048

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  • DOI: https://doi.org/10.1134/S2070046618020048

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