C. Bernard-michel, L. Gardes, and S. Girard, Gaussian Regularized Sliced Inverse Regression, Statistics and Computing, vol.5, issue.22, 2007.
DOI : 10.1007/s11222-008-9073-z
URL : https://hal.archives-ouvertes.fr/inria-00180458

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

J. 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

F. Chiaramonte and J. Martinelli, Dimension reduction strategies for analyzing global gene expression data with a response, Mathematical Biosciences, vol.176, issue.1, pp.123-144, 2002.
DOI : 10.1016/S0025-5564(01)00106-7

B. Combal, F. Baret, M. Weiss, A. Trubuil, D. Macé et al., Retrieval of canopy biophysical variables from bidirectional reflectance, Remote Sensing of Environment, vol.84, issue.1, pp.1-15, 2002.
DOI : 10.1016/S0034-4257(02)00035-4

R. D. Cook, Fisher Lecture: Dimension Reduction in Regression, Statistical Science, vol.22, issue.1, 2005.
DOI : 10.1214/088342306000000682

S. Douté, B. Schmitt, J. Bibring, Y. Langevin, F. Altieri et al., South Pole of Mars: Nature and composition of the icy terrains from Mars Express OMEGA observations, Planetary and Space Science, vol.55, issue.1-2, pp.113-133, 2007.
DOI : 10.1016/j.pss.2006.05.035

S. S. Durbha, R. L. King, and N. H. Younan, Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer, Remote Sensing of Environment, vol.107, issue.1-2, pp.348-361, 2007.
DOI : 10.1016/j.rse.2006.09.031

J. H. 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. Hadamard, Sur lesprobì emes aux dérivées partielles et leur signification physique. Princeton university bulletin, pp.49-52, 1920.

P. Hall and K. C. Li, On almost linearity of low dimensional projections from high dimensional data. The annals of Statistics, pp.867-889, 1993.

B. Kamgar-parsi and J. A. Gualtieri, Solving inversion problems with neural networks, 1990 IJCNN International Joint Conference on Neural Networks, pp.955-960, 1990.
DOI : 10.1109/IJCNN.1990.137966

D. S. Kimes, Y. Knyazikhin, J. L. Privette, A. A. Abuegasim, and F. Gao, Inversion methods for physically???based models, Remote Sensing Reviews, vol.1, issue.2-4, pp.381-439, 2000.
DOI : 10.1109/99.735892

K. C. Li, Sliced Inverse Regression for Dimension Reduction, Journal of the American Statistical Association, vol.13, issue.414, pp.316-327, 1991.
DOI : 10.1214/aos/1176345514
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.452.161

L. Li and H. Li, Dimension reduction methods for microarrays with application to censored survival data, Bioinformatics, vol.20, issue.18, pp.3406-3412, 2004.
DOI : 10.1093/bioinformatics/bth415

L. Li and X. Yin, Sliced Inverse Regression with Regularizations, Biometrics, vol.67, issue.1
DOI : 10.1111/j.1541-0420.2007.00836.x

C. D. Mobley, L. K. Sundman, C. O. Davis, M. Montes, and W. P. Bissett, A look-uptable approach to inverting remotely sensed ocean color data. Ocean Optics XVI, Office of Naval Research Ocean, Atmosphere, and Space Department, 2002.

K. Mosegaard and A. Tarantola, Probabilistic approach to inverse problems. International Handbook of Earthquake and Engineering Seismology, pp.237-265, 2002.

W. Philpot, C. Davis, W. P. Bisset, C. D. Mobley, D. D. Kholer et al., Bottom Characterization from Hyperspectral Image Data, Oceanography, vol.17, issue.2, pp.76-85, 2004.
DOI : 10.5670/oceanog.2004.50
URL : http://ir.library.oregonstate.edu/xmlui/bitstream/1957/37230/1/DavisCurtissOEarthOceanAtmosphericSciencesBottomCharacterizationFrom.pdf

G. Saporta, Probabilités, analyse des données et statistique, ´ edition révisée et augmentée, 2006.

F. Schmidt, S. Douté, and B. Schmitt, WAVANGLET: An Efficient Supervised Classifier for Hyperspectral Images, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.5, 2007.
DOI : 10.1109/TGRS.2006.890577
URL : https://hal.archives-ouvertes.fr/hal-00325458

B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization , Optimization, and Beyond, 2002.

L. Scrucca, Regularized sliced inverse regression with applications in classification. Data Analysis, Classification and the Forward Search, pp.59-66, 2006.

J. Shawe-taylor and N. Cristianini, Kernel Methods for Pattern Analysis, 2004.
DOI : 10.1017/CBO9780511809682

A. Tarantola, Inverse problem theory and model parameter estimation, SIAM, 2005.
DOI : 10.1137/1.9780898717921

A. Tarantola, Mapping of probabilities -Theory for the interpretation of uncertain physical measurements

C. R. Vogel, Computational methods for inverse problems, Society for Industrial and Applied Mathematics, 2002.
DOI : 10.1137/1.9780898717570

B. Wilamowski, Neural network architectures and learning, IEEE International Conference on Industrial Technology, 2003, 2003.
DOI : 10.1109/ICIT.2003.1290197

W. Zhong, P. Zeng, P. Ma, J. S. 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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.610.6339

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