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Principal symmetric space analysis
1. | Department of Mathematical Sciences, NTNU, Gjøvik, Norway |
2. | School of Mathematics and Statistics, Victoria University of Wellington, Wellington, New Zealand |
3. | School of Fundamental Sciences, Massey University, Palmerston North, New Zealand |
Principal Geodesic Analysis is a statistical technique that constructs low-dimensional approximations to data on Riemannian manifolds. It provides a generalization of principal components analysis to non-Euclidean spaces. The approximating submanifolds are geodesic at a reference point such as the intrinsic mean of the data. However, they are local methods as the approximation depends on the reference point and does not take into account the curvature of the manifold. Therefore, in this paper we develop a specialization of principal geodesic analysis, Principal Symmetric Space Analysis, based on nested sequences of totally geodesic submanifolds of symmetric spaces. The examples of spheres, Grassmannians, tori, and products of two-dimensional spheres are worked out in detail. The approximating submanifolds are geometrically the simplest possible, with zero exterior curvature at all points. They can deal with significant curvature and diverse topology. We show that in many cases the distance between a point and the submanifold can be computed analytically and there is a related metric that reduces the computation of principal symmetric space approximations to linear algebra.
References:
[1] |
J. Berndt and C. Olmos,
Maximal totally geodesic submanifolds and index of symmetric spaces, J. Differential Geom., 104 (2016), 187-217.
doi: 10.4310/jdg/1476367055. |
[2] |
B. Y. Chen and T. Nagano,
Totally geodesic submanifolds of symmetric spaces, Ⅱ, Duke Math J., 45 (1978), 405-425.
doi: 10.1215/S0012-7094-78-04521-0. |
[3] |
J. H. Conway, R. H. Hardin and N. J. Sloane,
Packing lines, planes, etc.: Packings in Grassmannian spaces, Experiment. Math., 5 (1996), 139-159.
doi: 10.1080/10586458.1996.10504585. |
[4] |
J. Damon and J. S. Marron,
Backwards principal component analysis and principal nested relations, J. Math. Imaging Vision, 50 (2014), 107-114.
doi: 10.1007/s10851-013-0463-2. |
[5] |
B. Eltzner, S. Jung and S. Huckermann, Dimension reduction on polyspheres with application to skeletal representations, in Geometric Science of Information, Lecture Notes in Comput. Sci., 9389, Springer, Cham, 2015, 22–29.
doi: 10.1007/978-3-319-25040-3_3. |
[6] |
P. T. Fletcher, C. Lu, S. M. Pizer and S. C. Joshi,
Principal geodesic analysis for the study of nonlinear statistics of shape, IEEE Transactions on Medical Imaging, 23 (2004), 995-1005.
doi: 10.1109/TMI.2004.831793. |
[7] |
P. T. Fletcher and S. C. Joshi, Principal geodesic analysis on symmetric spaces: Statistics of diffusion tensors, in Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis, LNCS, 3117, Springer, Berlin, 2004, 87–98. Google Scholar |
[8] |
T. Fletcher, Geodesic regression on Riemannian manifolds, in Proceedings of the Third International Workshop on Mathematical Foundations of Computational Anatomy-Geometrical and Statistical Methods for Modelling Biological Shape Variability, 2001, 75–86. Google Scholar |
[9] |
C. G. Gebhardt, M. C. Steinbach and R. Rolfes,
Understanding the nonlinear dynamics of beam structures: A principal geodesic analysis approach, Thin-Walled Structures, 140 (2019), 357-372.
doi: 10.1016/j.tws.2019.03.009. |
[10] |
G. H. Golub and C. F. Van Loan, Matrix Computations, Johns Hopkins Series in the Mathematical Sciences, 3, Johns Hopkins University Press, Baltimore, MD, 1989. |
[11] |
S. Huckemann, T. Hotz and A. Munk,
Intrinsic shape analysis: Geodesic PCA for Riemannian manifolds modulo isometric Lie group actions, Statist. Sinica, 20 (2010), 1-58.
|
[12] |
I. Jolliffe, Principal Component Analysis, Springer Series in Statistics, Springer-Verlag, New York, 1986.
doi: 10.1007/978-1-4757-1904-8. |
[13] |
S. Jung, I. L. Dryden and J. S. Marron,
Analysis of principal nested spheres, Biometrika, 99 (2012), 551-568.
doi: 10.1093/biomet/ass022. |
[14] |
K. Kenobi, I. L. Dryden and H. Le,
Shape curves and geodesic modelling, Biometrika, 97 (2010), 567-584.
doi: 10.1093/biomet/asq027. |
[15] |
S. Klein, Totally geodesic submanifolds in Riemannian symmetric spaces, in Differential Geometry, World Sci. Publ., Hackensack, NJ, 2009, 136–145.
doi: 10.1142/9789814261173_0013. |
[16] |
S. Kobayashi and K. Nomizu, Foundations of Differential Geometry, John Wiley & Sons, Inc., New York, 1996. |
[17] |
J. Lawrence,
Enumeration in torus arrangements, European J. Combin., 32 (2011), 870-881.
doi: 10.1016/j.ejc.2011.02.003. |
[18] |
A. K. Lenstra, H. W. Lenstra Jr. and L. Lovász,
Factoring polynomials with rational coefficients, Math. Ann., 261 (1982), 515-534.
doi: 10.1007/BF01457454. |
[19] |
X. Pennec,
Barycentric subspace analysis on manifolds, Ann. Statist., 46 (2018), 2711-2746.
doi: 10.1214/17-AOS1636. |
[20] |
Q. Rentmeesters, A gradient method for geodesic data fitting on some symmetric Riemannian manifolds, in 2011 50$^{th}$ IEEE Conference on Decision and Control and European Control Conference, 2011, 7141–7146.
doi: 10.1109/CDC.2011.6161280. |
[21] |
H. J. S. Smith, On systems of linear indeterminate equations and congruences, Phil. Trans. Roy. Soc. London, 151 (1861), 293-326. Google Scholar |
[22] |
S. Sommer, F. Lauze and M. Nielsen,
Optimization over geodesics for exact principal geodesic analysis, Adv. Comput. Math., 40 (2014), 283-313.
doi: 10.1007/s10444-013-9308-1. |
[23] |
W. Utschick,
Tracking of signal subspace projectors, IEEE Trans. Signal Process., 50 (2002), 769-778.
doi: 10.1109/78.992119. |
[24] |
J. A. Wolf,
Elliptic spaces in Grassmann manifolds, Illinois J. Math., 7 (1963), 447-462.
doi: 10.1215/ijm/1255644952. |
[25] |
D. Wubben, D. Seethaler, J. Jalden and G. Matz,
Lattice reduction, IEEE Signal Processing Magazine, 28 (2011), 70-91.
doi: 10.1109/MSP.2010.938758. |
show all references
References:
[1] |
J. Berndt and C. Olmos,
Maximal totally geodesic submanifolds and index of symmetric spaces, J. Differential Geom., 104 (2016), 187-217.
doi: 10.4310/jdg/1476367055. |
[2] |
B. Y. Chen and T. Nagano,
Totally geodesic submanifolds of symmetric spaces, Ⅱ, Duke Math J., 45 (1978), 405-425.
doi: 10.1215/S0012-7094-78-04521-0. |
[3] |
J. H. Conway, R. H. Hardin and N. J. Sloane,
Packing lines, planes, etc.: Packings in Grassmannian spaces, Experiment. Math., 5 (1996), 139-159.
doi: 10.1080/10586458.1996.10504585. |
[4] |
J. Damon and J. S. Marron,
Backwards principal component analysis and principal nested relations, J. Math. Imaging Vision, 50 (2014), 107-114.
doi: 10.1007/s10851-013-0463-2. |
[5] |
B. Eltzner, S. Jung and S. Huckermann, Dimension reduction on polyspheres with application to skeletal representations, in Geometric Science of Information, Lecture Notes in Comput. Sci., 9389, Springer, Cham, 2015, 22–29.
doi: 10.1007/978-3-319-25040-3_3. |
[6] |
P. T. Fletcher, C. Lu, S. M. Pizer and S. C. Joshi,
Principal geodesic analysis for the study of nonlinear statistics of shape, IEEE Transactions on Medical Imaging, 23 (2004), 995-1005.
doi: 10.1109/TMI.2004.831793. |
[7] |
P. T. Fletcher and S. C. Joshi, Principal geodesic analysis on symmetric spaces: Statistics of diffusion tensors, in Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis, LNCS, 3117, Springer, Berlin, 2004, 87–98. Google Scholar |
[8] |
T. Fletcher, Geodesic regression on Riemannian manifolds, in Proceedings of the Third International Workshop on Mathematical Foundations of Computational Anatomy-Geometrical and Statistical Methods for Modelling Biological Shape Variability, 2001, 75–86. Google Scholar |
[9] |
C. G. Gebhardt, M. C. Steinbach and R. Rolfes,
Understanding the nonlinear dynamics of beam structures: A principal geodesic analysis approach, Thin-Walled Structures, 140 (2019), 357-372.
doi: 10.1016/j.tws.2019.03.009. |
[10] |
G. H. Golub and C. F. Van Loan, Matrix Computations, Johns Hopkins Series in the Mathematical Sciences, 3, Johns Hopkins University Press, Baltimore, MD, 1989. |
[11] |
S. Huckemann, T. Hotz and A. Munk,
Intrinsic shape analysis: Geodesic PCA for Riemannian manifolds modulo isometric Lie group actions, Statist. Sinica, 20 (2010), 1-58.
|
[12] |
I. Jolliffe, Principal Component Analysis, Springer Series in Statistics, Springer-Verlag, New York, 1986.
doi: 10.1007/978-1-4757-1904-8. |
[13] |
S. Jung, I. L. Dryden and J. S. Marron,
Analysis of principal nested spheres, Biometrika, 99 (2012), 551-568.
doi: 10.1093/biomet/ass022. |
[14] |
K. Kenobi, I. L. Dryden and H. Le,
Shape curves and geodesic modelling, Biometrika, 97 (2010), 567-584.
doi: 10.1093/biomet/asq027. |
[15] |
S. Klein, Totally geodesic submanifolds in Riemannian symmetric spaces, in Differential Geometry, World Sci. Publ., Hackensack, NJ, 2009, 136–145.
doi: 10.1142/9789814261173_0013. |
[16] |
S. Kobayashi and K. Nomizu, Foundations of Differential Geometry, John Wiley & Sons, Inc., New York, 1996. |
[17] |
J. Lawrence,
Enumeration in torus arrangements, European J. Combin., 32 (2011), 870-881.
doi: 10.1016/j.ejc.2011.02.003. |
[18] |
A. K. Lenstra, H. W. Lenstra Jr. and L. Lovász,
Factoring polynomials with rational coefficients, Math. Ann., 261 (1982), 515-534.
doi: 10.1007/BF01457454. |
[19] |
X. Pennec,
Barycentric subspace analysis on manifolds, Ann. Statist., 46 (2018), 2711-2746.
doi: 10.1214/17-AOS1636. |
[20] |
Q. Rentmeesters, A gradient method for geodesic data fitting on some symmetric Riemannian manifolds, in 2011 50$^{th}$ IEEE Conference on Decision and Control and European Control Conference, 2011, 7141–7146.
doi: 10.1109/CDC.2011.6161280. |
[21] |
H. J. S. Smith, On systems of linear indeterminate equations and congruences, Phil. Trans. Roy. Soc. London, 151 (1861), 293-326. Google Scholar |
[22] |
S. Sommer, F. Lauze and M. Nielsen,
Optimization over geodesics for exact principal geodesic analysis, Adv. Comput. Math., 40 (2014), 283-313.
doi: 10.1007/s10444-013-9308-1. |
[23] |
W. Utschick,
Tracking of signal subspace projectors, IEEE Trans. Signal Process., 50 (2002), 769-778.
doi: 10.1109/78.992119. |
[24] |
J. A. Wolf,
Elliptic spaces in Grassmann manifolds, Illinois J. Math., 7 (1963), 447-462.
doi: 10.1215/ijm/1255644952. |
[25] |
D. Wubben, D. Seethaler, J. Jalden and G. Matz,
Lattice reduction, IEEE Signal Processing Magazine, 28 (2011), 70-91.
doi: 10.1109/MSP.2010.938758. |








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