Contributions à l'étude de la classification spectrale et applications

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162 BIBLIOGRAPHIE [55] D. Harel and Y. Koren. Clustering spatial data using random walks. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, page 286, 2001. [56] J.A. Hartigan and MA Wong. A k-means clustering algorithm, Algorithm AS 136. Applied Statistics, 28(1), 1979. [57] J. He, M. Lan, C.L. Tan, S.Y. Sung, and H.B. Low. Initialization of cluster refinement algorithms : a review and comparative study. In 2004 IEEE International Joint Conference on Neural Networks, 2004. Proceedings, volume 1, 2004. [58] R. Herbrich. Learning kernel classifiers : theory and algorithms. The MIT Press, 2002. [59] A. Hinneburg and D.A. Keim. An efficient approach to clustering in large multimedia databases with noise. Knowledge Discovery and Data Mining, 5865, 1998. [60] F. Hirsch and G. Lacombe. Elements of functional analysis. Springer, 1999. [61] AK Jain and RC Dubes. Algorithms for cluster analysis. Prentice Hall, Englewood Cliffs, NJ, 1988. [62] M. Kac. Can one hear the shape of a drum ? American Mathematical Monthly, pages 1–23, 1966. [63] R. Kannan and A. Vetta. On clusterings : Good, bad and spectral. Journal of the ACM, 51(3) :497–515, 2004. [64] V. Kapoor, B.M. McCook, and F.S. Torok. An Introduction to PET-CT Imaging1. Radiographics, 24(2) :523, 2004. [65] L. Kaufman and PJ Rousseeuw. Finding groups in data : an introduction to cluster analysis. NY John Wiley & Sons. [66] S.S. Khan and A. Ahmad. Cluster center initialization algorithm for K-means clustering. Pattern Recognition Letters, 25(11) :1293–1302, 2004. [67] Y. Kimura, M. Senda, and NM Alpert. Fast formation of statistically reliable FDG parametric images based on clustering and principal components. Physics in Medicine and Biology, 47 :455, 2002. [68] T. Kohonen. The self-organizing map. Neurocomputing, 21. [69] T. Kohonen, J. Hynninen, J. Kangas, and J. Laaksonen. SOM PAK : The self-organizing map program package. Report A31, Helsinki University of Technology, Laboratory of Computer and Information Science, 1996. [70] E. Krestyannikov, J. Tohka, and U. Ruotsalainen. Segmentation of dynamic emission tomography data in projection space. Computer Vision Approaches to Medical Image Analysis, pages 108–119, 2006. [71] JM Lambert and WT Williams. Multivariate methods in plant ecology : VI. Comparison of information-analysis and association-analysis. The Journal of Ecology, 54(3) :635–664, 1966. [72] GN Lance and WT Williams. A general theory of classificatory sorting strategies : 1. Hierarchical systems. The computer journal, 9(4) :373, 1967. [73] GN Lance and WT Williams. A general theory of classificatory sorting strategies : II. Clustering systems. The Computer Journal, 10(3) :271, 1967. [74] KW Lau, H. Yin, and S. Hubbard. Kernel self-organising maps for classification. Neurocomputing, 69(16-18) :2033–2040, 2006.
BIBLIOGRAPHIE 163 [75] R. Maroy, R. Boisgard, C. Comtat, V. Frouin, P. Cathier, E. Duchesnay, F. Dollé, P.E. Nielsen, R. Trébossen, and B. Tavitian. Segmentation of rodent whole-body dynamic PET images : an unsupervised method based on voxel dynamics. IEEE Transactions on Medical Imaging, 27(3) :342–354, 2008. [76] JC Mazziotta, CC Pelizzari, GT Chen, FL Bookstein, and D. Valentino. Region of interest issues : The relationship between structure and function in the brain. Journal of cerebral blood flow and metabolism, 11(2) :A51–A56, 1991. [77] JB Mcqueen. Some methods for classification and analysis of multivariate observations. u : Proceedings of Berkeley Symposium on mathematical statistics and probability. Berkeley, CA, itd : University of California Press, 1 :281–297, 1967. [78] M. Meilă. Comparing clusterings. In Proceedings of the Conference on Computational Learning Theory, 2003. [79] M. Meila and J. Shi. A random walks view of spectral segmentation. Artificial Intelligence and Statistics, 2001, 2001. [80] B. Nadler and M. Galun. Fundamental limitations of spectral clustering. Advances in Neural Information Processing Systems, 19 :1017, 2007. [81] B. Nadler, S. Lafon, R. Coifman, and I. Kevrekidis. Diffusion Maps-a Probabilistic Interpretation for Spectral Embedding and Clustering Algorithms. Principal Manifolds for Data Visualization and Dimension Reduction, pages 238–260. [82] B. Nadler, S. Lafon, R.R. Coifman, and I.G. Kevrekidis. Diffusion maps, spectral clustering and eigenfunctions of fokker-planck operators. Preprint, 2005. [83] B. Nadler, S. Lafon, R.R. Coifman, and I.G. Kevrekidis. Diffusion maps, spectral clustering and reaction coordinates of dynamical systems. Applied and Computational Harmonic Analysis, 21(1) :113–127, 2006. [84] A. Y. Ng, Michael I. Jordan, and Yair Weiss. On spectral clustering : analysis and an algorithm. Proceedings of Neural Information Processing Systems, 2002. [85] Freeman W.T. Perona, P. A factorization approach to grouping. European Conference on Computer Vision, 1998. [86] H. Queffélec. Topologie. Masson, 1998. [87] Thomas Raviart. Introduction à l’analyse numérique des équations aux dérivées partielles. Dunod, 1993. [88] E. Schikuta and M. Erhart. The BANG-clustering system : grid-based data analysis. Advances in Intelligent Data Analysis Reasoning about Data, pages 513–524, 1997. [89] G. Schwarz. Estimating the dimension of a model. The annals of statistics, 6(2) :461–464, 1978. [90] SZ Selim and M. Ismail. K-means-type algorithms : a generalized convergence theorem and characterization of local optimality. IEEE Transaction of Pattern Analysis and Machine intelligence, 6(1) :81–86, 1984. [91] J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8) :888–905, 2000. [92] Y. Song, W.Y. Chen, H. Bai, C.J. Lin, and E.Y. Chang. Parallel spectral clustering. In Proceedings of European Conference on Machine Learning and Pattern Knwoledge Discovery. Springer, 2008.
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Institut National Polytechnique de
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ii TABLE DES MATIÈRES 2.5.1 Expér
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Table des figures 1.1 Illustration
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TABLE DES FIGURES vii 4.8 Etude ave
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Remerciements Je tiens tout d’abo
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Introduction Les domaines des biolo
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adéquates dans un cadre non superv
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Chapitre 1 : Classification spectra
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Chapitre 1 Classification spectrale
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1.1.1 Algorithme de classification
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1.1.2 Problème du choix du paramè
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1.1.2 Problème du choix du paramè
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1.2.2 Cas d’une distribution isot
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1.3 Validations numériques 19 1.3
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1.3.1 Mesures de qualité 21 (a) Sm
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1.4 Méthodes de classification spe
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1.4.2 Traitement d’images 29 une
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1.4.2 Traitement d’images 31 (a)
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Chapitre 2 Classification et élém
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2.2 Présentation du résultat prin
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2.3 Propriétés de classification
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2.3.2 Classification via l’opéra
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2.4.1 Eléments finis de Lagrange 5
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2.4.2 Interprétation des élément
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2.4.3 Propriété de classification
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2.4.3 Propriété de classification
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2.4.4 Condensation de masse 61 Le t
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2.4.4 Condensation de masse 63 Eval
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2.4.4 Condensation de masse 65 (a)
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2.5.1 Expérimentations numériques
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2.5.2 Choix du paramètre gaussien
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2.5.3 Passage du discret au continu
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2.5.4 Etape de normalisation 77 d
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2.5.5 Cas limites de validité de l
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2.5.5 Cas limites de validité de l
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84 Parallélisation de la classific
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100 Parallélisation de la classifi
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Page 124 and 125: 112 Parallélisation de la classifi
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Page 167 and 168: Conclusion et perspectives Dans ce
Page 169 and 170: 4.9.4 Comparaison avec la méthode
Page 171 and 172: Bibliographie [1] P.D. Acton, L.S.
Page 173: BIBLIOGRAPHIE 161 [37] M. Ester, H.
Page 177: BIBLIOGRAPHIE 165 [114] L. Yen, D.
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