T. Ando, C. K. Li, and R. Mathias, Geometric means, Linear Algebra and its Applications, vol.385, pp.305-334, 2004.
DOI : 10.1016/j.laa.2003.11.019

M. Arnaudon, L. Yang, and F. Barbaresco, Stochastic algorithms for computing p-means of probability measures, geometry of Radar Toeplitz covariance matrices and applications to HR Doppler processing, International Radar Symposium (IRS), pp.651-656, 2011.

M. Arnaudon, F. Barbaresco, and L. Yang, Riemannian Medians and Means With Applications to Radar Signal Processing, IEEE Journal of Selected Topics in Signal Processing, vol.7, issue.4, pp.2013-595
DOI : 10.1109/JSTSP.2013.2261798

A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, Multiclass braincomputer interface classification by Riemannian geometry, IEEE Trans. Biomed. Eng, vol.59, issue.4, pp.2012-920
URL : https://hal.archives-ouvertes.fr/hal-00681328

R. H. Bartels, J. C. Beatty, and B. A. Barsky, Hermite and Cubic Spline Interpolation " , Ch. 3 in An Introduction to Splines for Use in Computer Graphics and Geometric Modelling, pp.9-17, 1998.

R. Bhatia, Positive Definite Matrices, 2007.
DOI : 10.1515/9781400827787
URL : https://hal.archives-ouvertes.fr/hal-01500514

H. Bouchard, Extremal positive splines with applications to interpolation and approximation by generalized convex functions, Bulletin of the American Mathematical Society, vol.79, issue.5, pp.959-963, 1973.
DOI : 10.1090/S0002-9904-1973-13278-1

M. Congedo, B. Asfari, A. Barachant, and M. Moakher, Approximate Joint Diagonalization and Geometric Mean of Symmetric Positive Definite Matrices, PLOS ONE, vol.26, issue.11, p.2015
DOI : 10.1371/journal.pone.0121423.t002
URL : https://hal.archives-ouvertes.fr/hal-01149652

G. Dong and G. Kuang, Target recognition in SAR images via classification on Riemannian manifolds, IEEE Geoscie. Remote Sens. Lett., vol, vol.21, issue.1, pp.2015-199

S. Hidot and C. , An Expectation???Maximization algorithm for the Wishart mixture model: Application to movement clustering, Pattern Recognition Letters, vol.31, issue.14, pp.2318-2324, 2010.
DOI : 10.1016/j.patrec.2010.07.002
URL : https://hal.archives-ouvertes.fr/hal-00718163

B. Jeuris, R. Vanderbril, and B. Vandereycken, A survey and comparison of contemporary algorithms for computing the matrix gepmetric mean, Elec. Trans. Numer. Anal, vol.39, pp.379-402, 2012.

J. S. Lee, M. R. Grunes, T. L. Ainsworth, L. J. Du, D. L. Schuler et al., Unsupervised classification using polarimetric decomposition and complex Wishart classifier, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174), pp.2249-2258, 1999.
DOI : 10.1109/IGARSS.1998.703778

Y. Li, K. Wong, and H. Debruin, EEG signal classification based on a riemannian distance measure, Proc. IEEE Toronto Int. Conf. Sci. Technol. Humanity, pp.268-273, 2009.

R. Luis-garcia, C. F. Westin, and C. Alberola-lopez, Gaussian mixtures on tensor fields for segmentation: Applications to medical imaging, Computerized Medical Imaging and Graphics, vol.35, issue.1, pp.16-30, 2011.
DOI : 10.1016/j.compmedimag.2010.09.001

M. Moakher, A Differential Geometric Approach to the Geometric Mean of Symmetric Positive-Definite Matrices, SIAM Journal on Matrix Analysis and Applications, vol.26, issue.3, pp.735-747, 2005.
DOI : 10.1137/S0895479803436937

M. Moakher, On the Averaging of Symmetric Positive-Definite Tensors, Journal of Elasticity, vol.38, issue.1, pp.273-296, 2006.
DOI : 10.1007/s10659-005-9035-z

X. Pennec, P. Fillard, and N. Ayache, A Riemannian Framework for Tensor Computing, International Journal of Computer Vision, vol.6, issue.2, pp.41-66, 2006.
DOI : 10.1007/s11263-005-3222-z
URL : https://hal.archives-ouvertes.fr/inria-00070743

X. Pennec, Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements, Journal of Mathematical Imaging and Vision, vol.20, issue.10, pp.127-154, 2006.
DOI : 10.1007/s10851-006-6228-4
URL : https://hal.archives-ouvertes.fr/inria-00614994

D. L. Russell, Computing Convex Spline Approximation, International Journal of Information and Systems Sciences, vol.5, pp.83-97, 2009.

S. Said, L. Bombrun, and Y. Berthoumieu, Texture Classification Using Rao???s Distance on the Space of Covariance Matrices, Conference on Geometric Science of Information, p.2015
DOI : 10.1007/978-3-319-25040-3_40
URL : https://hal.archives-ouvertes.fr/hal-01228766

A. Terras, Harmonic analysis on symmetric spaces and applications, 1988.
DOI : 10.1007/978-1-4612-5128-6

O. Tuzel, F. Porikli, and P. Meer, Pedestrian Detection via Classification on Riemannian Manifolds, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.10, pp.1713-1727, 2008.
DOI : 10.1109/TPAMI.2008.75

L. Zheng, G. Qiu, J. Huang, and J. Duan, Fast and accurate Nearest Neighbor search in the manifolds of symmetric positive definite matrices, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2014-3804
DOI : 10.1109/ICASSP.2014.6854313