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This paper is a strongly geometrical approach to the Fisher distance, which is a measure of dissimilarity between two probability distribution functions.
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A. M. Peter, A. Rangarajan, Information Geometry for Landmark Shape Analysis: Unifying Shape Representation and Deformation. IEEE Transactions on Pattern Analysis and Machine Intelligence , 31(2):337-350, 2009
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M. Liu, B. C. Vemuri, S. Amari, F. Nielsen, Total Bregman Divergence and its Applications to Shape Retrieval, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp.3463-3468, 2010
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2012
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S. I. R. Costa, S. A. Santos, J. E. Strapasson, Fisher information matrix and hyperbolic geometry, Proc. of IEEE ISOC ITW2005 on Coding and Complexity , pp.34-36, 2005
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K. M. Carter, R. Raich, A.O. Hero III, Learning on statistical manifolds for clustering and visualization, Proceedings of Forty-Fifth Annual Allerton Conference on Communication, Control, and Computing , 8p., 2007
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K. M. Carter, Dimensionality reduction on statistical manifolds , Ph.D. thesis, University of Michigan, January 2009
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F. Nielsen, R. Nock, Sided and Symmetrized Bregman Centroids, IEEE Transactions on Information Theory , 55(6) 2882-2904, 2009
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J. Angulo, S. Velasco-Forero, Morphological processing of univariate Gaussian distribution-valued images based on Poincaré upper-half plane representation. Available at http://hal-ensmp.archives-ouvertes.fr/hal-00795012 , v1, 27/February/2013
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L. Liberti, C. Lavor, N. Maculan, A. Mucherino. Euclidean distance geometry and applications. SIAM Review , 2013 (to appear)
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F. Nielsen, R. Bhatia, Matrix Information Geometry , DOI: http://dx.doi.org/10.1007/978-3-642-30232-9_16 , Springer-Verlag, Berlin, Heidelberg, 2013
2013
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A. Schutz, L. Bombrun, Y. Berthoumieu, K-Centroids-Based Supervised Classification of Texture Images Using the SIRV Modeling. In: Geometric Science of Information , F. Nielsen and F. Barbaresco (Eds.) First International Conference, GSI 2013 Paris, France, August 2013, Lecture Notes in Computer Science 8085 (2013), pp. 140-148
2013
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