L. Breiman, Random forests, Machine Learning, vol.45, pp.5-32, 2001.

L. Desboulets, A review on variable selection in regression analysis, Econometrics, vol.6, issue.4, p.45, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01954386

F. Ding-cheng, C. Feng, W. , and X. , Detecting local manifold structure for unsupervised feature selection, Acta Automatica Sinica, vol.40, issue.10, pp.2253-2261, 2014.

G. Doquet and M. Sebag, Agnostic feature selection, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, pp.343-358, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02436824

J. Fan and R. Li, Variable selection via nonconcave penalized likelihood and its oracle properties, Journal of the American Statistical Association, vol.96, pp.1348-60, 2001.

J. Fan and J. Lv, A selective overview of variable selection in high dimensional feature space, Statistica Sinica, vol.20, p.101, 2010.

J. Friedman, T. Hastie, and R. Tibshirani, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.9, issue.3, pp.432-441, 2008.

A. Gorban and A. Zinovyev, Principal graphs and manifolds, pp.28-59, 2011.

T. Hastie and W. Stuetzle, Principal curves, Journal of the American Statistical Association, vol.84, issue.406, pp.502-516, 1989.

T. K. Ho, Nearest neighbors in random subspaces, Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), pp.640-648, 1998.

K. Hornik, M. Stinchcombe, and H. White, Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks, Neural networks, vol.3, pp.551-560, 1990.

J. Huang, P. Breheny, M. , and S. , A selective review of group selection in highdimensional models, Statistical Science, p.27, 2012.

I. T. Jolliffe, A note on the use of principal components in regression, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.31, issue.3, pp.300-303, 1982.

A. Jovi?, K. Brki?, and N. Bogunovi?, A review of feature selection methods with applications. Paper presented at 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp.25-29, 2015.

T. Kohonen, The self-organizing map, Neurocomputing, vol.21, issue.1-3, pp.1-6, 1998.

M. Kramer, Nonlinear principal component analysis using autoassociative neural networks, AIChE Journal, vol.37, issue.2, pp.233-243, 1991.

J. Lafferty, H. Liu, and L. Wasserman, Sparse nonparametric graphical models, Statistical Science, vol.27, issue.4, pp.519-537, 2012.

Y. Li, X. Shi, C. Du, Y. Liu, W. et al., Manifold regularized multi-view feature selection for social image annotation, Neurocomputing, vol.204, pp.135-141, 2016.

T. Mehmood, K. H. Liland, L. Snipen, and S. Saebø, A review of variable selection methods in partial least squares regression, vol.118, pp.62-69, 2012.

N. Meinshausen and P. Bühlmann, Stability selection, Journal of the Royal Statistical Society: Series B, vol.72, pp.417-73, 2010.

S. Mika, B. Schölkopf, A. Smola, K. Müller, M. Scholz et al., Kernel pca and de-noising in feature spaces, pp.536-542, 1999.

R. Neuneier and H. G. Zimmermann, How to train neural networks, pp.373-423, 1998.

X. Ni, H. H. Zhang, and D. Zhang, Automatic model selection for partially linear models, Journal of Multivariate Analysis, vol.100, pp.2100-2111, 2009.

S. Roweis and L. Saul, Nonlinear dimensionality reduction by locally linear embedding, Science, vol.290, issue.5500, pp.2323-2326, 2000.

L. Saul and S. Roweis, Think globally, fit locally: unsupervised learning of low dimensional manifolds, Journal of Machine Learning Research, vol.4, pp.119-155, 2003.

B. Schölkopf, A. Smola, and K. Müller, Nonlinear component analysis as a kernel eigenvalue problem, Neural Computation, vol.10, issue.5, pp.1299-1319, 1998.

S. Sun and R. Huang, An adaptive k-nearest neighbor algorithm, 2010 seventh international conference on fuzzy systems and knowledge discovery, vol.1, pp.91-94, 2010.

C. Tang, M. Bian, X. Liu, M. Li, H. Zhou et al., Unsupervised feature selection via latent representation learning and manifold regularization, Neural Networks, vol.117, pp.163-178, 2019.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B, vol.58, pp.267-88, 1996.

N. Vecoven, Feature selection with deep neural networks, 2017.

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, Journal of Machine Learning Research, vol.11, pp.3371-3408, 2010.

H. Wang and Y. Xia, Shrinkage estimation of the varying coefficient model, Journal of the American Statistical Association, vol.104, pp.747-57, 2009.

D. M. Witten, R. Tibshirani, and T. Hastie, A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis, Biostatistics, vol.10, issue.3, pp.515-534, 2009.

M. Ye and Y. Sun, Variable selection via penalized neural network: a drop-out-one loss approach, International Conference on Machine Learning, pp.5620-5629, 2018.

H. Zou, T. Hastie, and R. Tibshirani, Sparse principal component analysis, Journal of Computational and Graphical Statistics, vol.15, issue.2, pp.265-286, 2006.