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Riemannian Gaussian Distributions on the Space of Positive-Definite Quaternion Matrices

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10589))

Abstract

Recently, Riemannian Gaussian distributions were defined on spaces of positive-definite real and complex matrices. The present paper extends this definition to the space of positive-definite quaternion matrices. In order to do so, it develops the Riemannian geometry of the space of positive-definite quaternion matrices, which is shown to be a Riemannian symmetric space of non-positive curvature. The paper gives original formulae for the Riemannian metric of this space, its geodesics, and distance function. Then, it develops the theory of Riemannian Gaussian distributions, including the exact expression of their probability density, their sampling algorithm and statistical inference.

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References

  1. Pennec, X.: Intrinsic statistics on Riemannian manifolds: basic tools for geometric measurements. J. Math. Imaging Vis. 25(1), 127–154 (2006)

    Article  MathSciNet  Google Scholar 

  2. Chebbi, Z., Moakher, M.: Means of Hermitian positive-definite matrices based on the log-determinant alpha-divergence function. Linear Algebra Appl. 436(7), 1872–1889 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  3. Helgason, S.: Differential Geometry, Lie Groups, and Symmetric Spaces. American Mathematical Society, Providence (2001)

    Book  MATH  Google Scholar 

  4. Besse, A.L.: Einstein Manifolds, 1st edn. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  5. Said, S., Bombrun, L., Berthoumieu, Y., Manton, J.H.: Riemannian Gaussian distributions on the space of symmetric positive definite matrices (accepted). IEEE Trans. Inf. Theory 63, 2153–2170 (2016)

    Article  MATH  Google Scholar 

  6. Said, S., Hajri, H., Bombrun, L., Vemuri, B.C.: Gaussian distributions on Riemannian symmetric spaces : statistical learning with structured covariance matrices (under review). IEEE Trans. Inf. Theory (2017, under review)

    Google Scholar 

  7. Flamant, J., Le Bihan, N., Chainais, P.: Time-frequency analysis of bivariate signals (under review). Applied and Computational Harmonic Analysis (2017)

    Google Scholar 

  8. Conway, J.H., Smith, D.A.: On Quaternions and Octonions, their Geometry, Arithmetic and Symmetry. CRC Press, Boca Raton (2003)

    MATH  Google Scholar 

  9. Zhang, F.: Quaternions and matrices of quaternions. Linear Algebra Appl. 251, 21–57 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  10. Kirillov, A.: An Introduction to Lie Groups and Lie Algebras. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  11. Le Bihan, N.: The geometry of proper quaternion random variables. Signal Processing (2017, to appear)

    Google Scholar 

  12. Robert, C.P., Casella, G.: Monte Carlo Statistical Methods. Springer, New York (2004). doi:10.1007/978-1-4757-4145-2

    Book  MATH  Google Scholar 

  13. Manton, J.H.: A globally convergent numerical algorithm for computing the centre of mass on compact Lie groups. In: ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004, vol. 3, pp. 2211–2216, December 2004

    Google Scholar 

  14. Manton, J.H.: A framework for generalising the Newton method and other iterative methods from Euclidean space to manifolds. Numer. Math. 129(1), 91–125 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  15. Zanini, P., Said, S., Congedo, M., Berthoumieu, Y., Jutten, C.: Parameter estimates of Riemannian Gaussian distributions in the manifold of covariance matrices. In: Sensor Array and Multichannel Signal Processsing Workshop (SAM) (2016)

    Google Scholar 

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Correspondence to Nicolas Le Bihan .

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Said, S., Le Bihan, N., Manton, J.H. (2017). Riemannian Gaussian Distributions on the Space of Positive-Definite Quaternion Matrices. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2017. Lecture Notes in Computer Science(), vol 10589. Springer, Cham. https://doi.org/10.1007/978-3-319-68445-1_82

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  • DOI: https://doi.org/10.1007/978-3-319-68445-1_82

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68444-4

  • Online ISBN: 978-3-319-68445-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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