J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu et al., Theano: a CPU and GPU math expression compiler, Proceedings of the Python for Scientific Computing Conference (SciPy), 2010.

S. Das and . Filters, wrappers and a boosting-based hybrid for feature selection, Proceedings of the Eighteenth International Conference on Machine Learning, ICML '01, pp.74-81, 2001.

Y. Freund and R. E. Schapire, Experiments with a new boosting algorithm, International Conference on Machine Learning, pp.148-156, 1996.

J. Gao, Q. Du, L. Gao, X. Sun, and B. Zhang, Ant colony optimization-based supervised and unsupervised band selections for hyperspectral urban data classification, Journal of Applied Remote Sensing, vol.8, issue.1, p.85094, 2014.
DOI : 10.1117/1.JRS.8.085094

S. Khalid, T. Khalil, and S. Nasreen, A survey of feature selection and feature extraction techniques in machine learning, 2014 Science and Information Conference, pp.372-378, 2014.
DOI : 10.1109/SAI.2014.6918213

J. Li and H. Liu, Challenges of Feature Selection for Big Data Analytics, IEEE Intelligent Systems, vol.32, issue.2, pp.9-15, 2017.
DOI : 10.1109/MIS.2017.38

P. Liu, H. Zhang, and K. B. Eom, Active Deep Learning for Classification of Hyperspectral Images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.10, issue.2, pp.712-724, 2017.
DOI : 10.1109/JSTARS.2016.2598859

G. Arroyo, M. Meja-lavalle, and E. Sucar, Feature selection with a perceptron neural net, In: Proceedings of the international workshop on feature selection for data mining, p.131135, 2006.

L. C. Molina, L. Belanche, and A. Nebot, Feature selection algorithms: a survey and experimental evaluation, 2002 IEEE International Conference on Data Mining, 2002. Proceedings., pp.306-313, 2002.
DOI : 10.1109/ICDM.2002.1183917

URL : http://upcommons.upc.edu/bitstream/2117/97413/1/R02-62.pdf

S. T. Monteiro and R. J. Murphy, Embedded feature selection of hyperspectral bands with boosted decision trees, 2011 IEEE International Geoscience and Remote Sensing Symposium, pp.2361-2364, 2011.
DOI : 10.1109/IGARSS.2011.6049684

P. M. Narendra and K. Fukunaga, A Branch and Bound Algorithm for Feature Subset Selection, IEEE Transactions on Computers, vol.26, issue.9, pp.917-922, 1977.
DOI : 10.1109/TC.1977.1674939

B. Pan, Z. Shi, and X. Xu, R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.10, issue.5, pp.1975-1986, 2017.
DOI : 10.1109/JSTARS.2017.2655516

B. Pan, Z. Shi, and X. Xu, R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.10, issue.5, pp.1975-1986, 2017.
DOI : 10.1109/JSTARS.2017.2655516

A. H. Shahana and V. Preeja, Survey on feature subset selection for high dimensional data, 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp.1-4, 2016.
DOI : 10.1109/ICCPCT.2016.7530147

S. Theodoridis and K. Koutroumbas, Pattern Recognition, Fourth Edition, 2008.

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res, vol.11, pp.3371-3408, 2010.