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

C. M. Bishop and M. Svensen, Robust Bayesian mixture modelling, Neurocomputing, vol.64, pp.235-252, 2005.

C. Archambeau and M. Verleysen, Robust Bayesian clustering, Neural Networks, vol.20, issue.1, pp.129-138, 2007.
DOI : 10.1016/j.neunet.2006.06.009

J. Sun, A. Kabán, and J. M. Garibaldi, Robust mixture clustering using Pearson type VII distribution, Pattern Recognition Letters, vol.31, issue.16, pp.2447-2454, 2010.
DOI : 10.1016/j.patrec.2010.07.015

J. L. Andrews and P. D. Mcnicholas, Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions, Statistics and Computing, vol.1, issue.4, pp.1021-1029, 2012.
DOI : 10.1007/s11222-011-9272-x

F. Forbes and D. Wraith, A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: application to robust clustering, Statistics and Computing, vol.94, issue.1, pp.971-984, 2014.
DOI : 10.1007/s11222-013-9414-4

S. Lee and G. Mclachlan, Finite mixtures of multivariate skew t-distributions: some recent and new results, Statistics and Computing, vol.82, issue.4, pp.181-202, 2014.
DOI : 10.1007/s11222-012-9362-4

B. Long, Z. M. Zhang, X. Wu, and P. S. Yu, Spectral clustering for multi-type relational data, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.585-592, 2006.
DOI : 10.1145/1143844.1143918

G. Tseng, Penalized and weighted K-means for clustering with scattered objects and prior information in high-throughput biological data, Bioinformatics, vol.23, issue.17, pp.2247-2255, 2007.
DOI : 10.1093/bioinformatics/btm320

M. Ackerman, S. Ben-david, S. Branzei, and D. Loker, Weighted clustering, Proceedings of AAAI, 2012.

D. Feldman and L. Schulman, Data reduction for weighted and outlierresistant clustering, Proceedings of the Twenty-Third Annual ACM- SIAM Symposium on Discrete Algorithms. SIAM, 2012, pp.1343-1354

]. F. Forbes, S. Doyle, D. Garcia-lorenzo, C. Barillot, and M. Dojat, A weighted multi-sequence Markov model for brain lesion segmentation, Proceedings of the International Conference on Artificial Intelligence and Statistics, pp.225-232, 2010.
URL : https://hal.archives-ouvertes.fr/inserm-00723808

M. A. Figueiredo and A. K. Jain, Unsupervised learning of finite mixture models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.3, pp.381-396, 2002.
DOI : 10.1109/34.990138

C. Hennig, Methods for merging gaussian mixture components Advances in Data Analysis and Classification, pp.3-34, 2010.

J. P. Baudry, E. A. Raftery, G. Celeux, K. Lo, and R. Gottardo, Combining Mixture Components for Clustering, Journal of Computational and Graphical Statistics, vol.19, issue.2, 2010.
DOI : 10.1198/jcgs.2010.08111

URL : https://hal.archives-ouvertes.fr/inria-00321090

V. Melnykov, Merging Mixture Components for Clustering Through Pairwise Overlap, Journal of Computational and Graphical Statistics, vol.1, issue.1, 2014.
DOI : 10.1080/01621459.1963.10500845

C. E. Rasmussen, The infinite Gaussian mixture model, NIPS, pp.554-560, 1999.

D. Gorur and C. Rasmussen, Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution, Journal of Computer Science and Technology, vol.25, issue.3, pp.653-664, 2010.
DOI : 10.1007/s11390-010-9355-8

H. Z. Yerebakan, B. Rajwa, and M. Dundar, The infinite mixture of infinite Gaussian mixtures, Advances in Neural Information Processing Systems, pp.28-36, 2014.

X. Wei and C. Li, The infinite Student's t-mixture for robust modeling, Signal Processing, vol.92, issue.1, pp.224-234, 2012.
DOI : 10.1016/j.sigpro.2011.07.010

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

G. Celeux, S. Chrétien, F. Forbes, and A. Mkhadri, A Component-Wise EM Algorithm for Mixtures, Journal of Computational and Graphical Statistics, vol.10, issue.4, 2001.
DOI : 10.1198/106186001317243403

URL : https://hal.archives-ouvertes.fr/inria-00072916

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2324, 1998.
DOI : 10.1109/5.726791

L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and Regression Trees, 1984.

W. Street, W. Wolberg, and O. Mangasarian, Nuclear feature extraction for breast tumor diagnosis, IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology. International Society for Optics and Photonics, pp.861-870, 1993.

P. W. Frey and D. J. Slate, Letter recognition using Holland-style adaptive classifiers, Machine Learning, pp.161-182, 1991.
DOI : 10.1007/BF00114162

C. M. Bishop, Pattern Recognition and Machine Learning, 2006.

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

I. S. Dhillon, Y. Guan, and B. Kulis, Kernel k-means, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.551-556, 2004.
DOI : 10.1145/1014052.1014118

J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.888-905, 2000.

Y. Zhao and G. Karypis, Evaluation of hierarchical clustering algorithms for document datasets, Proceedings of the eleventh international conference on Information and knowledge management , CIKM '02, pp.515-524, 2002.
DOI : 10.1145/584792.584877

D. Davies and D. Bouldin, A Cluster Separation Measure, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.1, issue.2, pp.224-227, 1979.
DOI : 10.1109/TPAMI.1979.4766909

A. Deleforge, R. Horaud, Y. Y. Schechner, and L. Girin, Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol.23, issue.4, pp.718-731, 2015.
DOI : 10.1109/TASLP.2015.2405475

URL : https://hal.archives-ouvertes.fr/hal-01112834

V. Ferrari, M. Marin-jimenez, and A. Zisserman, Progressive search space reduction for human pose estimation, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587468

X. Zhu and D. Ramanan, Face detection, pose estimation, and landmark localization in the wild, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2012.

J. Sohn, N. S. Kim, and W. Sung, A statistical model-based voice activity detection, IEEE Signal Processing Letters, vol.6, issue.1, pp.1-3, 1999.
DOI : 10.1109/97.736233