Skip to main content

Geometry on Positive Definite Matrices Induced from V-Potential Function

  • Conference paper
Geometric Science of Information (GSI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8085))

Included in the following conference series:

Abstract

In this paper we investigate the dually flat structure of the space of positive definite matrices induced by the class of convex functions called V-potentials from the viewpoints of information geometry. It is proved that the geometry is invariant under special linear group actions. As an application to statistics, we finally give the correspondence between the obtained geometry on positive definite matrices and the one on elliptical distributions induced from a certain Bregman divergence.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amari, S.: Differential-geometrical methods in statistics. Lecture notes in statist., vol. 28. Springer, New York (1985)

    Book  MATH  Google Scholar 

  2. Amari, S., Nagaoka, H.: Methods of information geometry. AMS&OUP (2000)

    Google Scholar 

  3. David, A.P.: The geometry of proper scoring rules. Ann. Inst. Stat. 59, 77–93 (2007)

    Article  Google Scholar 

  4. Eguchi, S.: Information geometry and statistical pattern recognition. Sugaku Expositions, Amer. Math. Soc. 19, 197–216 (2006); Originally Sūgaku, 56, 380–399 (2004) (in Japanese)

    Google Scholar 

  5. Eguchi, S.: Information divergence geometry and the application to statistical machine learning. In: Emmert-Streib, F., Dehmer, M. (eds.) Information Theory and Statistical Learning, pp. 309–332. Springer (2008)

    Google Scholar 

  6. Eguchi, S., Copas, J.: A class of logistic-type discriminant functions. Biometrika 89(1), 1–22 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  7. Eguchi, S., Komori, O., Kato, S.: Projective power entropy and maximum Tsallis entropy distributions. Entropy 13, 1746–1764 (2011)

    Article  MathSciNet  Google Scholar 

  8. Fang, K.T., Kotz, S., Ng, K.W.: Symmetric multivariate and related distributions. Chapman and Hall, London (1990)

    MATH  Google Scholar 

  9. Faraut, J., Korányi, A.: Analysis on symmetric cones. Oxford Univ. Press, New York (1994)

    MATH  Google Scholar 

  10. Grunwald, P.D., David, A.P.: Game theory, maximum entropy, minimum discrepancy and robust Bayesian decision theory. Ann. Stat. 32, 1367–1433 (2004)

    Article  Google Scholar 

  11. Helgason, S.: Differential geometry and symmetric spaces. Academic Press, New York (1962)

    MATH  Google Scholar 

  12. Kanamori, T., Ohara, A.: A Bregman Extension of quasi-Newton updates I: An Information Geometrical framework. Optimization Methods and Software 28(1), 96–123 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  13. Koecher, M.: The Minnesota notes on Jordan algebras and their applications. Springer, Berlin (1999)

    MATH  Google Scholar 

  14. Muirhead, R.J.: Aspects of multivariate statistical theory. Wiley, New York (1982)

    Book  MATH  Google Scholar 

  15. Murata, N., Takenouchi, T., Kanamori, T., Eguchi, S.: Information geometry of U-boost and Bregman divergence. Neural Computation 16, 1437–1481 (2004)

    Article  MATH  Google Scholar 

  16. Naudts, J.: Continuity of a class of entropies and relative entropies. Rev. Math. Phys. 16, 809–822 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  17. Naudts, J.: Estimators, escort probabilities, and φ-exponential families in statistical physics. J. Ineq. Pure Appl. Math. 5, 102 (2004)

    MathSciNet  Google Scholar 

  18. Ohara, A.: Geodesics for dual connections and means on symmetric cones. Integral Equations and Operator Theory 50, 537–548 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  19. Ohara, A., Suda, N., Amari, S.: Dualistic differential geometry of positive definite matrices and its applications to related problems. Linear Algebra Appl. 247, 31–53 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  20. Ollila, E., Tyler, D., Koivunen, V., Poor, V.: Complex Elliptically Symmetric Distributions: Survey, New Results and Applications. IEEE Trans. Signal Process. 60(11), 5597–5623 (2012)

    Article  MathSciNet  Google Scholar 

  21. Rothaus, O.S.: Domains of positivity. Abh. Math. Sem. Univ. Hamburg 24, 189–235 (1960)

    Article  MathSciNet  MATH  Google Scholar 

  22. Scott, D.W.: Parametric statistical modeling by minimum integrated square error. Technometrics 43, 274–285 (2001)

    Article  MathSciNet  Google Scholar 

  23. Shima, H.: The geometry of Hessian structures. World Scientific, Singapore (2007)

    Book  MATH  Google Scholar 

  24. Tsallis, C.: Introduction to Nonextensive Statistical Mechanics. Springer, New York (2009)

    MATH  Google Scholar 

  25. Vinberg, E.B.: The theory of convex homogeneous cones. Trans. Moscow Math. Soc. 12, 340–430 (1963)

    MATH  Google Scholar 

  26. Wolkowicz, H., et al. (eds.): Handbook of semidefinite programming. Kluwer Acad. Publ., Boston (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ohara, A., Eguchi, S. (2013). Geometry on Positive Definite Matrices Induced from V-Potential Function. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2013. Lecture Notes in Computer Science, vol 8085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40020-9_69

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40020-9_69

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40019-3

  • Online ISBN: 978-3-642-40020-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics