T. Hofmann, B. Schölkpof, and A. J. Smola, Kernel methods in machine learning, The annals of statistics, pp.1171-1220, 2008.
DOI : 10.1214/009053607000000677

G. Camps-valls and L. Bruzzone, Kernel Methods for Remote Sensing Data Analysis, 2009.
DOI : 10.1002/9780470748992

V. Vapnik, The Nature of Statistical Learning Theory, 1999.

P. Williams, S. Li, J. Feng, and S. Wu, A Geometrical Method to Improve Performance of the Support Vector Machine, IEEE Transactions on Neural Networks, vol.18, issue.3, pp.942-947, 2007.
DOI : 10.1109/TNN.2007.891625

A. Villa, M. Fauvel, J. Chanussot, P. Gamba, and J. A. Benediktsson, Gradient Optimization for multiple kernel's parameters in support vector machines classification, IGARSS 2008, 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008.
DOI : 10.1109/IGARSS.2008.4779698

G. Camps-valls, A. Rodrigo-gonzalez, J. Muoz-mari, L. Gomez-chova, and J. Calpe-maravilla, Hyperspectral image classification with mahalanobis relevance vector machines, 2007 IEEE International Geoscience and Remote Sensing Symposium, pp.3802-3805, 2007.
DOI : 10.1109/IGARSS.2007.4423671

S. Abe, Training of support vector machines with Mahalanobis kernels, Artificial Neural Networks: Formal Models and Their Applications -ICANN 2005, Lecture Notes in Computer Science, pp.571-576, 2005.

C. R. Vogel, Computational Methods for Inverse Problems, Society for Industrial and Applied Mathematics, 2002.
DOI : 10.1137/1.9780898717570

C. Bernard-michel, S. Douté, M. Fauvel, L. Gardes, and S. Girard, Machine learning techniques for the inversion of planetary hyperspectrales images, Proc. of IEEE Int. Workshop on hyperspectral image and signal processing, 2009.

C. Bernard-michel, L. Gardes, and S. Girard, Gaussian Regularized Sliced Inverse Regression, Statistics and Computing, vol.5, issue.22, pp.85-98, 2009.
DOI : 10.1007/s11222-008-9073-z

URL : https://hal.archives-ouvertes.fr/inria-00180458

M. E. Tipping and C. M. Bishop, Probabilistic Principal Component Analysis, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.3, pp.611-622, 1999.
DOI : 10.1111/1467-9868.00196

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

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, Choosing multiple parameters for support vector machines, Machine Learning, pp.131-159, 2002.