. Nous-avons-comparé-sur-cette-base-notre-approche, «. Qui-sera-notée, +. Markov, «. [-a-i-b-i-q-i-d-i-]-», . Indep et al., + [a i b i Q i d i ], Markov + diag-GMM ». La notation « Indep. » traduit le fait que l'approche ne prend pas en compte les relations spatiales et la notation « diag-GMM » indique que le modèle parcimonieux diag-GMM a été utilisé

C. Annexe, Publications liées à la thèse Conférences nationales

?. J. Blanchet and C. Bouveyron, Modèle markovien caché pour la classification supervisée de données de grande dimension spatialement corrélées, 38èmes Journées de Statistique de la Société Française de Statistique, 2006.

?. C. Bouveyron, S. Girard, and C. Schmid, Classification des données de grande dimension : application à la vision par ordinateur. 2èmes Rencontres Inter-Associations sur la classification et ses applications, pp.24-25, 2006.

?. C. Bouveyron, S. Girard, and C. Schmid, Une nouvelle méthode de classification pour la reconnaissance de formes. 20ème colloque GRETSI sur le traitement du signal et des images, pp.711-714, 2005.

?. C. Bouveyron, S. Girard, and C. Schmid, Une méthode de classification des données de grande dimension, 2005.

?. C. Bouveyron, S. Girard, and C. Schmid, Dimension Reduction and Classification Methods for Object Recognition in Vision. 5th French-Danish Workshop on Spatial Statistics and Image Analysis in Biology, pp.109-113, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00548547

]. S. Bibliographie1, D. Agarwal, and . Roth, Learning a sparse representation for object detection, 7th European Conference on Computer Vision, pp.113-130, 2002.

C. Aggarwal, J. Wolf, P. Yu, and J. Park, Fast algorithms for projected clustering, International Conference on Management of Data, pp.61-72, 1999.
DOI : 10.1145/304182.304188
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.681.7363

R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, Automatic subspace clustering of highdimensional data for data mining application, ACM SIGMOD International Conference on Management of Data, pp.94-105, 1998.

A. Banerjee, C. Krumpelman, J. Ghosh, S. Basu, and R. Mooney, Model-based overlapping clustering, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining , KDD '05, pp.532-537, 2005.
DOI : 10.1145/1081870.1081932
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.75.3495

J. Banfield and A. Raftery, Model-Based Gaussian and Non-Gaussian Clustering, Biometrics, vol.49, issue.3, pp.803-821, 1993.
DOI : 10.2307/2532201

R. Bellman, Dynamic programming, 1957.

H. Bensmail and G. Celeux, Regularized Gaussian Discriminant Analysis through Eigenvalue Decomposition, Journal of the American Statistical Association, vol.91, issue.436, pp.1743-1748, 1996.
DOI : 10.1002/0471725293
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.1873

C. Biernacki, G. Celeux, and G. Govaert, Assessing a mixture model for clustering with the integrated completed likelihood, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.7, pp.719-725, 2000.
DOI : 10.1109/34.865189

C. Biernacki, G. Celeux, G. Govaert, and F. Langrognet, Model-based cluster and discriminant analysis with the MIXMOD software, Computational Statistics & Data Analysis, vol.51, issue.2, 2006.
DOI : 10.1016/j.csda.2005.12.015
URL : https://hal.archives-ouvertes.fr/inria-00069878

C. Bishop and M. Tipping, A hierarchical latent variable model for data visualization, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.3, pp.281-293, 1998.
DOI : 10.1109/34.667885

J. Blanchet, F. Forbes, and C. Schmid, Markov random fields for textures recognition with local invariant regions and their geometric relationships, Procedings of the British Machine Vision Conference 2005, 2005.
DOI : 10.5244/C.19.72
URL : https://hal.archives-ouvertes.fr/inria-00548520

L. Bocci, D. Vicari, and M. Vichi, A mixture model for the classification of three-way proximity data, Computational Statistics & Data Analysis, vol.50, issue.7, pp.1625-1654, 2006.
DOI : 10.1016/j.csda.2005.02.007

G. Bouchard and G. Celeux, Selection of generative models in classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.4, pp.544-554, 2005.
DOI : 10.1109/TPAMI.2006.82

C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.121-167, 1998.
DOI : 10.1023/A:1009715923555

M. Carreira-perpinan, A review of dimension reduction techniques, 1997.

G. Celeux and J. Diebolt, The SEM algorithm : a probabilistic teacher algorithm from the EM algorithm for the mixture problem, Computational Statistics Quarterly, vol.2, issue.1, pp.73-92, 1985.

G. Celeux and J. Diebolt, Une version de type recuit simul de l'algorithme EM, Notes aux Comptes Rendus de l'Acad mie des Sciences, pp.119-124, 1990.

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

G. Celeux and G. Govaert, Gaussian parsimonious clustering models, Pattern Recognition, vol.28, issue.5, pp.781-793, 1995.
DOI : 10.1016/0031-3203(94)00125-6
URL : https://hal.archives-ouvertes.fr/inria-00074643

J. Davis and M. Goadrich, The relationship between Precision-Recall and ROC curves, Proceedings of the 23rd international conference on Machine learning , ICML '06, 2006.
DOI : 10.1145/1143844.1143874

P. Delicado, Another Look at Principal Curves and Surfaces, Journal of Multivariate Analysis, vol.77, issue.1, pp.84-116, 2001.
DOI : 10.1006/jmva.2000.1917
URL : http://doi.org/10.1006/jmva.2000.1917

P. Demartines and J. Hérault, Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets, IEEE Transactions on Neural Networks, vol.8, issue.1, pp.148-154, 1997.
DOI : 10.1109/72.554199

A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, vol.39, issue.1, pp.1-38, 1977.

D. Donoho, High-dimensional data analysis : the curses and blessings of dimensionality, Math Challenges of the 21st Century, 2000.

G. Dorko and C. Schmid, Object class recognition using discriminative local features, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00070510

T. Fawcett, ROC graphs : notes and practical considerations for researchers, 2004.

C. Ferry and J. Hernandez-orallo, Volume under the ROC Surface for Multi-class Problems, 14th European Conference on Machine Learning, pp.108-120, 2003.
DOI : 10.1007/978-3-540-39857-8_12

E. Fix and J. Hodges, Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties, International Statistical Review / Revue Internationale de Statistique, vol.57, issue.3, 1951.
DOI : 10.2307/1403797

B. Flury and W. Gautschi, An Algorithm for Simultaneous Orthogonal Transformation of Several Positive Definite Symmetric Matrices to Nearly Diagonal Form, SIAM Journal on Scientific and Statistical Computing, vol.7, issue.1, pp.169-184, 1986.
DOI : 10.1137/0907013

B. Flury, M. Schmid, and A. Narayanan, Error rates in quadratic discrimination with constraints on the covariance matrices, Journal of Classification, vol.77, issue.1, pp.101-120, 1994.
DOI : 10.1007/BF01201025

L. Flury, B. Boukai, and B. Flury, The Discrimination Subspace Model, Journal of the American Statistical Association, vol.17, issue.438, pp.758-766, 1997.
DOI : 10.1080/01621459.1997.10474028

I. Fodor, A survey of dimension reduction techniques, 2002.
DOI : 10.2172/15002155

C. Fraley and A. Raftery, Model-Based Clustering, Discriminant Analysis, and Density Estimation, Journal of the American Statistical Association, vol.97, issue.458, pp.611-631, 2002.
DOI : 10.1198/016214502760047131
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.5734

Y. Freund and R. Shapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, vol.55, issue.1, pp.119-139, 1997.
DOI : 10.1006/jcss.1997.1504

J. Friedman, Regularized Discriminant Analysis, Journal of the American Statistical Association, vol.33, issue.405, pp.165-175, 1989.
DOI : 10.1080/01621459.1989.10478752

J. Friedman, Another approach to polychotomous classification, 1996.

Z. Ghahramani and G. Hinton, The EM algorithm for mixtures of factor analyzers, 1996.

S. Girard and S. Iovleff, Auto-associative models and generalized principal component analysis, Journal of Multivariate Analysis, vol.93, issue.1, pp.21-39, 2005.
DOI : 10.1016/j.jmva.2004.01.006
URL : https://hal.archives-ouvertes.fr/hal-00985337

G. Govaert, Analyse des données. Traitement du signal et de l'image, Hermes Science, 2003.

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003.

H. Harman, Modern factor analysis, 1976.

T. Hastie, A. Buja, and R. Tibshirani, Penalized Discriminant Analysis, The Annals of Statistics, vol.23, issue.1, pp.73-102, 1995.
DOI : 10.1214/aos/1176324456

T. Hastie and W. Stuetzle, Principal Curves, Journal of the American Statistical Association, vol.26, issue.406, pp.502-516, 1989.
DOI : 10.1080/03610927508827223

T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learning, 2001.

T. Hofmann, Probabilistic latent semantic analysis, Proc. of Uncertainty in Artificial Intelligence , UAI'99, 1999.

P. Huber, Projection pursuit. The Annals of Statistics, pp.435-525, 1985.

I. Jolliffe, Principal component analysis, 1986.
DOI : 10.1007/978-1-4757-1904-8

T. Kadir, A. Zisserman, and M. Brady, An Affine Invariant Salient Region Detector, 8th European Conference on Computer Vision, 2004.
DOI : 10.1007/978-3-540-24670-1_18

W. Krzanowski, P. Jonathan, W. Mccarthy, and M. Thomas, Discriminant Analysis with Singular Covariance Matrices: Methods and Applications to Spectroscopic Data, Applied Statistics, vol.44, issue.1, pp.101-115, 1995.
DOI : 10.2307/2986198

J. W. Lee, J. B. Lee, M. Park, and S. Song, An extensive comparison of recent classification tools applied to microarray data, Computational Statistics & Data Analysis, vol.48, issue.4, pp.869-885, 2005.
DOI : 10.1016/j.csda.2004.03.017

R. Lehoucq, D. Sorensen, and C. Yang, ARPACK users' guide : solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods, 1998.
DOI : 10.1137/1.9780898719628

B. Leibe and B. Schiele, Interleaved Object Categorization and Segmentation, Procedings of the British Machine Vision Conference 2003, 2003.
DOI : 10.5244/C.17.78
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.103.3586

D. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.91-110, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94

J. Malick, A Dual Approach to Semidefinite Least-Squares Problems, SIAM Journal on Matrix Analysis and Applications, vol.26, issue.1, pp.272-284, 2004.
DOI : 10.1137/S0895479802413856
URL : https://hal.archives-ouvertes.fr/inria-00072409

G. Mclachlan and D. Peel, Finite Mixture Models, 2000.
DOI : 10.1002/0471721182

G. Mclachlan, D. Peel, and R. Bean, Modelling high-dimensional data by mixtures of factor analyzers, Computational Statistics & Data Analysis, vol.41, issue.3-4, pp.379-388, 2001.
DOI : 10.1016/S0167-9473(02)00183-4

K. Mikolajczyk and C. Schmid, A performance evaluation of local descriptors, IEEE Conference on Computer Vision and Pattern Recognition, pp.257-263, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00548227

K. Mikolajczyk and C. Schmid, Scale & Affine Invariant Interest Point Detectors, International Journal of Computer Vision, vol.60, issue.1, pp.63-86, 2004.
DOI : 10.1023/B:VISI.0000027790.02288.f2
URL : https://hal.archives-ouvertes.fr/inria-00548554

A. Mkhadri, G. Celeux, and A. Nasrollah, Regularization in discriminant analysis: an overview, Computational Statistics & Data Analysis, vol.23, issue.3, pp.403-423, 1997.
DOI : 10.1016/S0167-9473(96)00043-6

P. Moerland, A comparison of mixture models for density estimation Artificial Neural Networks, [64] B. Moghaddam. Principal manifolds and probabilistic subspaces for visual recognition, Pattern Analysis and Machine Intelligence, vol.26, issue.6, pp.780-788, 2002.

D. Mossman, Three-way ROCs, Medical Decision Making, vol.19, issue.1, pp.78-89, 1999.
DOI : 10.1177/0272989X9901900110

T. O. Neill, Error rates of non-Bayes classification rules and the robustness of Fisher's linear discriminant function, Biometrika, vol.79, issue.1, pp.177-184, 1992.
DOI : 10.1093/biomet/79.1.177

A. Opelt, M. Fussenegger, A. Pinz, and P. Auer, Weak Hypotheses and Boosting for Generic Object Detection and Recognition, European Conference on Computer Vision, pp.71-84, 2004.
DOI : 10.1007/978-3-540-24671-8_6
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.219.3175

L. Parsons, E. Haque, and H. Liu, Subspace clustering for high dimensional data, ACM SIGKDD Explorations Newsletter, vol.6, issue.1
DOI : 10.1145/1007730.1007731

T. Pavlenko, On feature selection, curse-of-dimensionality and error probability in discriminant analysis, Journal of Statistical Planning and Inference, vol.115, issue.2, pp.565-584, 2003.
DOI : 10.1016/S0378-3758(02)00166-0

T. Pavlenko and D. Von-rosen, Effect of dimensionality on discrimination, Statistics, vol.9, issue.3, pp.191-213, 2001.
DOI : 10.1016/0031-3203(90)90100-Y

K. Pearson, Contributions to the Mathematical Theory of Evolution, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.185, issue.0, pp.71-110, 1894.
DOI : 10.1098/rsta.1894.0003

I. Pima and M. Aladjem, Regularized discriminant analysis for face recognition, Pattern Recognition, vol.37, issue.9, pp.1945-1948, 2004.
DOI : 10.1016/j.patcog.2004.03.011

A. Raftery and N. Dean, Variable Selection for Model-Based Clustering, Journal of the American Statistical Association, vol.101, issue.473, pp.168-178, 2006.
DOI : 10.1198/016214506000000113

S. Roweis and Z. Ghahramani, A Unifying Review of Linear Gaussian Models, Neural Computation, vol.45, issue.3, pp.305-345, 1999.
DOI : 10.1109/TIT.1967.1054010

S. Roweis and L. Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, vol.290, issue.5500, pp.2323-2326, 2000.
DOI : 10.1126/science.290.5500.2323
URL : http://astro.temple.edu/~msobel/courses_files/saulmds.pdf

D. Rubin and D. Thayer, EM algorithms for ML factor analysis, Psychometrika, vol.34, issue.1, pp.69-76, 1982.
DOI : 10.1007/BF02293851

B. Schölkopf, A. Smola, and K. Müller, Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation, vol.20, issue.5, pp.1299-1319, 1998.
DOI : 10.1007/BF02281970

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

D. Scott, Multivariate density estimation, 1992.

D. Scott and J. Thompson, Probability density estimation in higher dimensions, Fifteenth Symposium in the Interface, pp.173-179, 1983.

B. Silverman, Density estimation for Statistics and data analysis, 1986.
DOI : 10.1007/978-1-4899-3324-9

J. Sivic, B. Russell, A. Efros, A. Zisserman, and W. Freeman, Discovering objects and their location in images, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005.
DOI : 10.1109/ICCV.2005.77

J. Tenenbaum, V. D. Silva, and J. Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, vol.290, issue.5500, pp.2319-2323, 2000.
DOI : 10.1126/science.290.5500.2319

M. Tipping and C. Bishop, Probabilistic Principal Component Analysis, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.3, 1997.
DOI : 10.1111/1467-9868.00196

M. Tipping and C. Bishop, Mixtures of Probabilistic Principal Component Analyzers, Neural Computation, vol.2, issue.1, pp.443-482, 1999.
DOI : 10.1007/BF00162527

V. Vapnik, The nature of statistical learning theory, 1996.

M. Verleysen, Learning high-dimensional data Limitations and Future Trends in Neural Computations, pp.141-162, 2003.

A. Webb, Statistical pattern recognition, 2002.
DOI : 10.1002/9781119952954

J. Willamowski, D. Arregui, G. Csurka, C. Dance, and L. Fan, Coategorizing nine visual classes using local appareance descriptors, International Workshop on Learning for Adaptable Visual Systems, 2004.

C. Wu, On the Convergence Properties of the EM Algorithm, The Annals of Statistics, vol.11, issue.1, pp.95-103, 1983.
DOI : 10.1214/aos/1176346060

J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, Local features and kernels for classification of texture and object categories, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548574

W. Zhong, P. Zeng, P. Ma, J. Liu, and Y. Zhu, RSIR: regularized sliced inverse regression for motif discovery, Bioinformatics, vol.21, issue.22, pp.4169-4175, 2005.
DOI : 10.1093/bioinformatics/bti680