@. Olivier, S. , and F. Nielsen, pyMEF: A framework for exponential families in Python, 2011 IEEE Statistical Signal Processing Workshop (SSP). 2011, pp.669-672

@. Olivier, S. , and F. Nielsen, Non-flat clustering with alphadivergences, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2011, pp.2100-2103, 2011.

S. @bullet-frank-nielsen, O. Boltz, and . Schwander, Bhattacharyya Clustering with Applications to Mixture Simplifications, 20th International Conference on Pattern Recognition (ICPR). 2010, pp.1437-1440, 2010.

@. Olivier, S. , and F. Nielsen, Reranking with Contextual Dissimilarity Measures from Representational Bregman k-Means In: VISAPP (1), pp.118-123, 2010.

@. Olivier, S. , and F. Nielsen, Simplification de modèles de mélange issus d'estimateur par noyau, 2011.

@. Olivier, S. , and F. Nielsen, Apprentissage rapide de modèles de mélanges avec k-MLE et ses extensions, 2013.

]. J. Aczél, On mean values, Bulletin of the American Mathematical Society, vol.54, issue.4, pp.392-400, 1948.
DOI : 10.1090/S0002-9904-1948-09016-4

S. M. Ali and S. D. Silvey, A General Class of Coefficients of Divergence of One Distribution from Another, In: Journal of the Royal Statistical Society. Series B (Methodological), vol.28, issue.1, pp.131-142, 1966.

M. S. Allili, Wavelet-Based Texture Retrieval Using a Mixture of Generalized Gaussian Distributions, 2010 20th International Conference on Pattern Recognition, pp.3143-3146, 2010.
DOI : 10.1109/ICPR.2010.769

M. S. Allili, D. Ziou, N. Bouguila, and S. Boutemedjet, Image and Video Segmentation by Combining Unsupervised Generalized Gaussian Mixture Modeling and Feature Selection, IEEE Transactions on Circuits and Systems for Video Technology, vol.20, issue.10, pp.1373-1377, 2010.
DOI : 10.1109/TCSVT.2010.2077483

J. Almhana, Z. Liu, V. Choulakian, and R. Mcgorman, A Recursive Algorithm for Gamma Mixture Models, 2006 IEEE International Conference on Communications, pp.197-202, 2006.
DOI : 10.1109/ICC.2006.254727

S. Amari, Differential Geometry of Curved Exponential Families-Curvatures and Information Loss, The Annals of Statistics, vol.10, issue.2, pp.357-385, 1982.
DOI : 10.1214/aos/1176345779

. Shun-ichi, H. Amari, and . Nagaoka, Methods of Information Geometry . en, pp.14-40, 2000.

T. Amin, M. Zeytinoglu, and L. Guan, Application of Laplacian Mixture Model to Image and Video Retrieval, IEEE Transactions on Multimedia, vol.9, issue.7, pp.1416-1429, 2007.
DOI : 10.1109/TMM.2007.906587

Y. Amit and A. Trouvé, Generative Models for Labeling Multiobject Configurations in Images In: Toward Category-Level Object Recognition, Lecture Notes in Computer Science, vol.4170

. Erling-bernhard-andersen, Sufficiency and Exponential Families for Discrete Sample Spaces, In: Journal of the American Statistical Association, vol.65331, 1970.

A. Banerjee, I. S. Dhillon, J. Ghosh, and S. Sra, Clustering on the unit hypersphere using von mises-fisher distributions, In: Journal of Machine Learning Research, vol.6, p.2005, 2005.

A. Banerjee, S. Merugu, I. S. Dhillon, and J. Ghosh, Clustering with Bregman Divergences, In: J. Mach. Learn. Res, vol.6, issue.101 110, pp.1705-1749, 2005.
DOI : 10.1137/1.9781611972740.22

O. B. Nielsen, Information and exponential families : in statistical theory Wiley series in probability and mathematical statistics. Chichester [etc, 1978.

M. Basseville, Divergence measures for statistical data processing???An annotated bibliography, Signal Processing, vol.93, issue.4, 2012.
DOI : 10.1016/j.sigpro.2012.09.003
URL : https://hal.archives-ouvertes.fr/hal-01151803

E. Leonard, T. Baum, and . Petrie, Statistical Inference for Probabilistic Functions of Finite State Markov Chains, In: The Annals of Mathematical Statistics, vol.37, issue.6, pp.1554-1563, 1966.

A. Ben-tal, A. Charnes, and M. Teboulle, Entropic means, Journal of Mathematical Analysis and Applications, vol.139, issue.2, pp.537-551, 1989.
DOI : 10.1016/0022-247X(89)90128-5

J. Bernauer, X. Huang, Y. L. Adelene, M. Sim, and . Levitt, Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation, RNA, vol.17, issue.6, pp.1066-1075, 2011.
DOI : 10.1261/rna.2543711
URL : https://hal.archives-ouvertes.fr/inria-00624999

A. Bhattacharyya, On a measure of divergence between two statistical populations defined by their probability distributions, In: Bull. Calcutta Math. Soc, vol.35, pp.99-109, 1943.

D. Bhowmick, A. C. Davison, D. R. Goldstein, and Y. Ruffieux, A Laplace mixture model for identification of differential expression in microarray experiments, Biostatistics, vol.7, issue.4, pp.630-641, 2006.
DOI : 10.1093/biostatistics/kxj032

C. Biernacki, G. Celeux, G. Govaert, and F. Langrognet, Modelbased cluster and discriminant analysis with the MIXMOD software, In: Computational Statistics & Data Analysis, vol.512, pp.587-600, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00069878

K. Bogdan and M. Bogdan, On Existence of Maximum Likelihood Estimators in Exponential Families, Statistics, vol.20, issue.2, pp.137-149, 2000.
DOI : 10.1214/aos/1176350710

J. Boissonnat, F. Nielsen, and R. Nock, Bregman Voronoi Diagrams, Discrete & Computational Geometry, vol.12, issue.2, p.48, 2007.
DOI : 10.1007/s00454-010-9256-1
URL : https://hal.archives-ouvertes.fr/hal-00488441

A. W. Bowman, An alternative method of cross-validation for the smoothing of density estimates, Biometrika 71, pp.353-360, 1984.
DOI : 10.1093/biomet/71.2.353

P. Brodatz, Textures: a photographic album for artists and designers, 1966.

L. D. Brown, Fundamentals of Statistical Exponential Families: With Applications in Statistical Decision Theory. en, IMS, vol.25, p.22, 1986.

J. Burbea and C. Rao, On the convexity of some divergence measures based on entropy functions, IEEE Transactions on Information Theory, vol.28, issue.3, pp.489-495, 1982.
DOI : 10.1109/TIT.1982.1056497

G. Celeux and G. Govaert, A classification EM algorithm for clustering and two stochastic versions, Computational Statistics & Data Analysis, vol.14, issue.3, pp.315-332, 1992.
DOI : 10.1016/0167-9473(92)90042-E
URL : https://hal.archives-ouvertes.fr/inria-00075196

P. Chen, Y. Chen, and M. Rao, Metrics defined by Bregman Divergences, Communications in Mathematical Sciences, vol.6, issue.4, pp.915-926, 2008.
DOI : 10.4310/CMS.2008.v6.n4.a6

N. Nikolaevich and C. , Statistical Decision Rules and Optimal Inference. en, American Mathematical Soc, vol.14, issue.27, p.73, 1982.

S. Choy and C. Tong, Supervised Texture Classification Using Characteristic Generalized Gaussian Density, Journal of Mathematical Imaging and Vision, vol.2, issue.4, pp.35-47, 2007.
DOI : 10.1007/s10851-007-0023-8

A. Cichocki, S. Cruces, and S. Amari, Generalized Alpha-Beta Divergences and Their Application to Robust Nonnegative Matrix Factorization, Entropy, vol.13, issue.12, pp.134-170, 2011.
DOI : 10.3390/e13010134

L. Cobb, The Multimodal Exponential Families of Statistical Catastrophe Theory en In: Statistical Distributions in Scientific Work NATO Advanced Study Institutes Series 79, pp.67-90, 1981.

S. I. Costa, S. A. Santos, and J. E. Strapasson, Fisher information matrix and hyperbolic geometry, IEEE Information Theory Workshop, 2005., pp.3-74
DOI : 10.1109/ITW.2005.1531851

H. Cramér, Mathematical methods of statistics. en, 1946.

I. Csiszár, Information-type measures of difference of probability distributions and indirect observations, In: Studia Sci. Math. Hungar, vol.2, pp.299-318, 1967.

G. Darmois, Sur les lois de probabilites à estimation exhaustive, In: C.R. Acad. Sci. Paris, vol.200, pp.1265-1266, 1935.

S. Dasgupta, J. Matus, and . Telgarsky, Agglomerative Bregman Clustering, Proceedings of the 29th International Conference on Machine Learning (ICML-12). 2012, pp.1527-1534

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, In: Journal of the Royal Statistical Society. Series BMethodological), vol.39, issue.1, pp.1-38, 1977.

A. Dessein, Méthodes Computationnelles en Géométrie de l'Information et Applications Temps Réel au Traitement du Signal Audio, 2012.

M. Deza and E. Deza, Encyclopedia of Distances. en, 2009.

M. N. Do and M. Vetterli, Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance, IEEE Transactions on Image Processing, vol.11, issue.2, pp.146-158, 2002.
DOI : 10.1109/83.982822

J. Durrieu, J. Thiran, and F. Kelly, Lower and upper bounds for approximation of the Kullback-Leibler divergence between Gaussian Mixture Models, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4833-4836, 2012.
DOI : 10.1109/ICASSP.2012.6289001

J. Edmonds and R. M. Karp, Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems, Journal of the ACM, vol.19, issue.2, pp.248-264, 1972.
DOI : 10.1145/321694.321699

V. A. Epanechnikov, Non-Parametric Estimation of a Multivariate Probability Density, Theory of Probability & Its Applications, vol.14, issue.1, pp.153-158, 1969.
DOI : 10.1137/1114019

R. A. Fisher, Two New Properties of Mathematical Likelihood, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.144, issue.852, pp.285-307, 1934.
DOI : 10.1098/rspa.1934.0050

M. Fréchet, Sur l'extension de certaines evaluations statistiques au cas de petits echantillons, Revue de l'Institut International de Statistique / Review of the International Statistical Institute, vol.11, issue.3/4, pp.182-213, 1943.
DOI : 10.2307/1401114

G. A. Galperin, A concept of the mass center of a system of material points in the constant curvature spaces, Communications in Mathematical Physics, vol.65, issue.No. 5, pp.63-84, 1993.
DOI : 10.1007/BF02096832

V. Garcia, F. Nielsen, and R. Nock, Levels of Details for Gaussian Mixture Models, In: Computer Vision?ACCV, vol.70, pp.514-525, 2009.
DOI : 10.1007/978-3-642-12304-7_48

B. Georgi, I. G. Costa, and A. Schliep, PyMix - The Python mixture package - a tool for clustering of heterogeneous biological data, BMC Bioinformatics, vol.11, issue.1, p.9, 2010.
DOI : 10.1186/1471-2105-11-9

B. Gough, GNU Scientific Library Reference Manual -Third Edition. 3rd, Network Theory Ltd, 2009.

R. M. Gray, A. Buzo, J. Gray, A. , and Y. Matsuyama, Distortion measures for speech processing, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.28, issue.4, pp.367-376, 1980.
DOI : 10.1109/TASSP.1980.1163421

L. R. Haff, P. T. Kim, J. Koo, D. St, and P. Richards, Minimax estimation for mixtures of Wishart distributions, The Annals of Statistics, vol.39, issue.6, pp.3417-3440, 2011.
DOI : 10.1214/11-AOS951

P. Hall, J. S. Marron, and B. U. Park, Smoothed cross-validation, Probability Theory and Related Fields 92, pp.1-20, 1992.
DOI : 10.1007/BF01205233

J. A. Hartigan and M. A. Wong, Algorithm AS 136: A K-Means Clustering Algorithm, Applied Statistics, vol.28, issue.1, p.100, 1979.
DOI : 10.2307/2346830

W. K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, issue.1, pp.97-109, 1970.
DOI : 10.1093/biomet/57.1.97

E. Hellinger, Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen, German, 1909.

J. R. Hershey and P. A. Olsen, Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, pp.317-320, 2007.
DOI : 10.1109/ICASSP.2007.366913

H. Hotelling, Spaces of statistical parameters, In: Bull. Amer. Math. Soc, vol.36, p.191, 1930.

C. Jacobi, Theoria novi multiplicatoris systemati aequationum differentialium vulgarium applicandi, In: Journal für die reine und angewandte Mathematik, vol.27, pp.199-268, 1844.

H. Jeffreys, An Invariant Form for the Prior Probability in Estimation Problems, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.186, issue.1007, pp.1007-453, 1946.
DOI : 10.1098/rspa.1946.0056

F. Jelinek, Continuous speech recognition by statistical methods, Proceedings of the IEEE, vol.64, issue.4, pp.532-556, 1976.
DOI : 10.1109/PROC.1976.10159

R. Jenssen, J. C. Principe, D. Erdogmus, and T. Eltoft, The Cauchy???Schwarz divergence and Parzen windowing: Connections to graph theory and Mercer kernels, Journal of the Franklin Institute, vol.343, issue.6, pp.614-629, 2006.
DOI : 10.1016/j.jfranklin.2006.03.018

K. Kampa, E. Hasanbelliu, and J. C. Principe, Closed-form cauchy-schwarz PDF divergence for mixture of Gaussians, The 2011 International Joint Conference on Neural Networks, pp.2578-2585
DOI : 10.1109/IJCNN.2011.6033555

E. Robert and . Kass, The geometry of asymptotic inference, In: Statistical Science, pp.188-219, 1989.

E. Robert, P. W. Kass, and . Vos, Geometrical Foundations of Asymptotic Inference. en, 2011.

R. W. Keener, Curved Exponential Families en. In: Theoretical Statistics. Springer Texts in Statistics, pp.85-99, 2010.

F. Klein, Vergleichende Betrachtungen ???ber neuere geometrische Forschungen, Mathematische Annalen, vol.43, issue.1, pp.63-100, 1893.
DOI : 10.1007/BF01446615

A. Kolmogorov, Sur la notion de la moyenne, In: Atti Accad. Naz. Lincei, vol.12, pp.388-391, 1930.

A. Koloydenko, M. Käärik, and J. Lember, On Adjusted Viterbi Training, Acta Applicandae Mathematicae, vol.6, issue.2, pp.1-3, 2007.
DOI : 10.1007/s10440-007-9102-5

B. O. Koopman, On distributions admitting a sufficient statistic, Transactions of the American Mathematical Society, vol.39, issue.3, p.399, 1936.
DOI : 10.1090/S0002-9947-1936-1501854-3

H. W. Kuhn, The Hungarian method for the assignment problem, Naval Research Logistics Quarterly, vol.2, pp.1-2, 1955.

J. Lafferty and G. Lebanon, Diffusion Kernels on Statistical Manifolds, pp.129-163, 2005.

J. Li and H. Zha, Two-way Poisson mixture models for simultaneous document classification and word clustering, Computational Statistics & Data Analysis, vol.50, issue.1, pp.163-180, 2006.
DOI : 10.1016/j.csda.2004.07.013

M. Liu, B. C. Vemuri, S. Amari, and F. Nielsen, Shape Retrieval Using Hierarchical Total Bregman Soft Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.3412, pp.2407-2419, 2012.

S. P. Lloyd, Least squares quantization in PCM, IEEE Transactions on Information Theory, vol.28, issue.2, pp.129-137, 1982.
DOI : 10.1109/TIT.1982.1056489

L. Hông-vân, The uniqueness of the Fisher metric as information metric. arXiv e-print 1306.1465, pp.27-73, 2013.

P. Mahalanobis, On the generalised distance in statistics, pp.49-55, 1936.

S. G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.117, issue.111, pp.674-693, 1989.

K. Matusita, Decision Rules, Based on the Distance, for Problems of Fit, Two Samples, and Estimation, The Annals of Mathematical Statistics, vol.26, issue.4, pp.631-640, 1955.
DOI : 10.1214/aoms/1177728422

I. Mayrose, N. Friedman, and T. Pupko, A Gamma mixture model better accounts for among site rate heterogeneity, Bioinformatics, vol.21, issue.Suppl 2, 2005.
DOI : 10.1093/bioinformatics/bti1125

G. Mclachlan and D. Peel, Finite Mixture Models. en, 2004.

N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, Equation of State Calculations by Fast Computing Machines, The Journal of Chemical Physics, vol.21, issue.6, pp.1087-1092, 1953.
DOI : 10.1063/1.1699114

T. Morimoto, -Theorem, Journal of the Physical Society of Japan, vol.18, issue.3, p.328, 1963.
DOI : 10.1143/JPSJ.18.328
URL : https://hal.archives-ouvertes.fr/hal-00658784

J. Munkres, Algorithms for the Assignment and Transportation Problems, Journal of the Society for Industrial and Applied Mathematics, vol.5, issue.1, pp.32-38, 1957.
DOI : 10.1137/0105003

S. Nadarajah, A generalized normal distribution, Journal of Applied Statistics, vol.1, issue.7, pp.685-694, 2005.
DOI : 10.1016/S0378-3758(00)00169-5

M. Nagumo, Uber eine klasse der mittelwerte, In: Japanese Journal of Mathematics, vol.7, pp.71-79, 1930.

M. Radford and . Neal, Slice sampling, Ann. Stat, vol.313, pp.705-767, 2003.

M. Radford, G. E. Neal, and . Hinton, A View of the EM Algorithm that Justifies Incremental, Sparse, and other Variants Learning in Graphical Models, NATO ASI Series, vol.89, pp.355-368, 1998.

F. Nielsen, Closed-form information-theoretic divergences for statistical mixtures, 2012 21st International Conference on Pattern Recognition (ICPR). 2012, pp.1723-1726

F. Nielsen, K-MLE: A fast algorithm for learning statistical mixture models, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.869-872, 2012.
DOI : 10.1109/ICASSP.2012.6288022

F. Nielsen, J. D. Boissonnat, and R. Nock, On Bregman Voronoi diagrams, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pp.755-769, 2007.

F. Nielsen and S. Boltz, The Burbea-Rao and Bhattacharyya Centroids, IEEE Transactions on Information Theory, vol.578, issue.46, pp.5455-5466, 2011.

F. Nielsen and R. Nock, Hyperbolic Voronoi Diagrams Made Easy, 2010 International Conference on Computational Science and Its Applications, p.76, 2009.
DOI : 10.1109/ICCSA.2010.37

F. Nielsen and R. Nock, Sided and symmetrized Bregman centroids, IEEE Transactions on Information Theory, vol.556, pp.2882-2904, 2009.

F. Nielsen, P. Piro, and M. Barlaud, Bregman vantage point trees for efficient nearest Neighbor Queries, 2009 IEEE International Conference on Multimedia and Expo, pp.878-881, 2009.
DOI : 10.1109/ICME.2009.5202635
URL : https://hal.archives-ouvertes.fr/hal-00481723

F. Nielsen, K-MLE: A fast algorithm for learning statistical mixture models, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
DOI : 10.1109/ICASSP.2012.6288022

F. Nielsen and R. Bhatia, Matrix Information Geometry. en, 2012.

F. Nielsen, S. Boltz, and O. Schwander, Bhattacharyya Clustering with Applications to Mixture Simplifications, 2010 20th International Conference on Pattern Recognition, pp.1437-1440, 2010.
DOI : 10.1109/ICPR.2010.355

F. Nielsen and V. Garcia, Statistical exponential families: A digest with flash cards, pp.73-79, 2009.

E. Parzen, On Estimation of a Probability Density Function and Mode, The Annals of Mathematical Statistics, vol.33, issue.3, pp.1065-1076, 1962.
DOI : 10.1214/aoms/1177704472

B. Pelletier, Informative barycentres in statistics, Annals of the Institute of Statistical Mathematics, vol.11, issue.3, pp.767-780, 2005.
DOI : 10.1007/BF02915437

P. Piro, Learning prototype-based classification rules in a boosting framework: application to real-world and medical image categorization, 2010.
URL : https://hal.archives-ouvertes.fr/tel-00590403

E. J. Pitman, Sufficient statistics and intrinsic accuracy, Mathematical Proceedings of the Cambridge Philosophical Society, vol.22, issue.04, pp.4-567, 1936.
DOI : 10.1098/rspa.1934.0050

C. and R. Rao, Information and the Accuracy Attainable in the Estimation of Statistical Parameters, In: Bulletin of the Calcutta Mathematical Society, vol.373, issue.25, pp.81-91, 1945.
DOI : 10.1007/978-1-4612-0919-5_16

C. E. Rasmussen, The infinite Gaussian mixture model In: Advances in Neural Information Processing Systems, pp.554-560, 2000.

F. Reverter and J. Oller, Computing the Rao distance for Gamma distributions, Journal of Computational and Applied Mathematics, vol.157, issue.1, pp.155-167, 2003.
DOI : 10.1016/S0377-0427(03)00387-X

R. Tyrrell-rockafellar, Convex Analysis. en, 1970.

G. Rong, M. Jin, and X. Guo, Hyperbolic centroidal Voronoi tessellation, Proceedings of the 14th ACM Symposium on Solid and Physical Modeling, SPM '10, pp.117-126, 2010.
DOI : 10.1145/1839778.1839795

M. Rosenblatt, Remarks on Some Nonparametric Estimates of a Density Function, The Annals of Mathematical Statistics, vol.27, issue.3, pp.832-837, 1956.
DOI : 10.1214/aoms/1177728190

C. Saint-jean and F. Nielsen, A New Implementation of k-MLE for Mixture Modeling of Wishart Distributions, 2013.
DOI : 10.1007/978-3-642-40020-9_26
URL : https://hal.archives-ouvertes.fr/hal-00841987

A. Schutz, L. Bombrun, and Y. Berthoumieu, kcentroids-based supervised classification of texture images: handling the intra-class diversity, IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.1498-1502, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00841939

O. Schwander and F. Nielsen, Apprentissage rapide de modèles de mélanges avec k-MLE et ses extensions, 2013.

O. Schwander and F. Nielsen, Fast Learning of Gamma Mixture Models with k-MLE, Lecture Notes in Computer Science, vol.7953, issue.149, pp.235-249, 2013.
DOI : 10.1007/978-3-642-39140-8_16

O. Schwander and F. Nielsen, Learning Mixtures by Simplifying Kernel Density Estimators, pp.403-426, 2013.
DOI : 10.1007/978-3-642-30232-9_16

O. Schwander and F. Nielsen, Model centroids for the simplification of Kernel Density estimators, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.737-740
DOI : 10.1109/ICASSP.2012.6287989

O. Schwander and F. Nielsen, Non-flat clustering with alphadivergences, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2011, pp.2100-2103, 2011.

O. Schwander and F. Nielsen, PyMEF — A framework for exponential families in Python, 2011 IEEE Statistical Signal Processing Workshop (SSP), pp.669-672, 2011.
DOI : 10.1109/SSP.2011.5967790

O. Schwander and F. Nielsen, Reranking with Contextual Dissimilarity Measures from Representational Bregman k-Means In: VISAPP (1), pp.118-123, 2010.

O. Schwander and F. Nielsen, Simplification de modèles de mélange issus d'estimateur par noyau, 2011.

O. Schwander, A. Schutz, F. Nielsen, and Y. Berthoumieu, k-MLE for mixtures of generalized Gaussians, 2012 21st International Conference on Pattern Recognition (ICPR). 2012, pp.2825-2828

J. C. Seabra, F. Ciompi, O. Pujol, J. Mauri, P. Radeva et al., Rayleigh Mixture Model for Plaque Characterization in Intravascular Ultrasound, IEEE Transactions on Biomedical Engineering, vol.58, issue.5, pp.1314-1324, 2011.
DOI : 10.1109/TBME.2011.2106498

S. J. Sheather and M. C. Jones, A Reliable Data-Based Bandwidth Selection Method for Kernel Density Estimation, In: Journal of the Royal Statistical Society. Series B (Methodological), vol.533, issue.68, pp.683-690, 1991.

Y. L. Adelene, O. Sim, M. Schwander, J. Levitt, and . Bernauer, Evaluating Mixture Models for Building RNA Knowledge-Based Potentials, In: Journal of Bioinformatics and Computational Biology, vol.10, issue.92, pp.2-149, 2012.

S. Venturini, F. Dominici, and G. Parmigiani, Gamma shape mixtures for heavy-tailed distributions, MathSciNet): MR2524355, pp.756-776, 2008.
DOI : 10.1214/08-AOAS156SUPP

F. Wang, T. Syeda-mahmood, C. Baba, D. Vemuri, A. Beymer et al., Closed-Form Jensen-Renyi Divergence for Mixture of Gaussians and Applications to Group-Wise Shape Registration, Lecture Notes in Computer Science, vol.5761, pp.648-655, 2009.
DOI : 10.1007/978-3-642-04268-3_80

A. Weber, Über den Standort der Industrien Erster Teil: Reine Theorie des Standorts, 1909.