C. Anderson, The End of Theory: The Data Deluge Makes the Scientific Method Obsolete, 2008.

G. Baudat and F. Anouar, Generalized discriminant analysis using a kernel approach, Neural Computation, vol.12, pp.2385-2404, 2000.

A. Bernard, C. Guinot, and G. Saporta, Sparse principal component analysis for multiblock data and its extension to sparse multiple correspondence analysis, Proceedings of 20th International Conference on Computational Statistics, pp.99-106, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01126171

P. Besse, Spline functions and optimal metric in linear principal components analysis, Components and Correspondence Analysis, 1988.

L. Billard and E. Diday, Symbolic Data Analysis: Conceptual Statistics and Data Mining, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00360427

L. Bottou, Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising, Journal of Machine Learning Research, vol.14, pp.3207-3260, 2013.

S. Bougeard, H. Abdi, G. Saporta, and N. Niang-keita, Clusterwise analysis for multiblock component methods, Advances in Data Analysis and Classification, vol.12, pp.285-313, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02470765

G. Box, J. S. Hunter, and W. G. Hunter, Statistics for Experimenters, Statistical Modeling: The Two Cultures, vol.16, pp.199-231, 1978.

F. Cailliez and J. P. Pagès, Introduction à l'analyse des données, 1976.

J. D. Carroll, Generalisation of canonical correlation analysis to three or more sets of variables. Proceedings, 76 th Annual Convention, vol.3, pp.227-228, 1968.

H. Chun and S. Kele?, Sparse partial least squares regression for simultaneous dimension reduction and variable selection, Journal of the Royal Statistical Society: Series B, vol.72, pp.3-25, 2010.

L. Clemmensen, T. Hastie, and K. Ersboell, Sparse discriminant analysis, Technometrics, vol.53, pp.406-413, 2011.

D. Costanzo, C. Preda, and G. Saporta, Anticipated prediction in discriminant analysis on functional data for binary response, COMPSTAT'06, A, pp.821-828, 2006.
URL : https://hal.archives-ouvertes.fr/hal-01125209

K. De-roover, E. Ceulemans, M. E. Timmerman, K. Vansteelandt, J. Stouten et al., Clusterwise simultaneous component analysis for analyzing structural differences in multivariate multiblock data, Psychol Methods, vol.17, pp.100-119, 2012.

C. De-la, O. Holmes, and S. P. , The Duality Diagram in Data Analysis: Examples of Modern Applications, Annals of Applied Statistics, vol.5, pp.2266-2277, 2011.

J. C. Deville, Méthodes statistiques et numériques de l'analyse harmonique, Annales de l'INSEE, vol.15, pp.3-101, 1974.

J. C. Deville and G. Saporta, Analyse harmonique qualitative, Data Analysis and Informatics, E.Diday, pp.375-389, 1980.

E. Diday, Introduction à l'analyse factorielle typologique, Revue de Statistique Appliquée, vol.22, pp.29-38, 1974.

D. Donoho, 50 Years of Data Science, Journal of Computational and Graphical Statistics, vol.26, pp.745-766, 2017.

J. H. Friedman, The Role of Statistics in the Data Revolution?, International Statistical Review, vol.69, pp.5-10, 2001.

C. Fyfe and P. L. Lai, Kernel and nonlinear canonical correlation analysis, International Journal of Neural Systems, vol.10, pp.365-374, 2001.

A. Gifi, Non-linear multivariate analysis, 1990.

D. Hand, G. Blunt, M. Kelly, and N. Adams, Data mining for fun and profit, Statistical Science, vol.15, pp.111-126, 2000.

D. Hand, Classifier Technology and the Illusion of Progress, Statistical Science, vol.21, pp.1-14, 2006.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2001.

M. Keller and J. Neufeld, Terms of Service: Understanding Our Role in the World of Big Data, 2014.

K. Hornik, Are There Too Many R Packages?, Australian Journal of Statistics, vol.41, pp.59-66, 2012.

D. Lazer, R. Kennedy, G. King, and A. Vespignani, The Parable of Google Flu: Traps in Big Data Analysis, vol.343, pp.1203-1205, 2014.

L. Lebart, A. Salem, and L. Berry, Exploring Textual Data, 1998.

F. Marcotorchino, Maximal association as a tool for classification, Classification as a tool for research, pp.275-288, 1986.

J. A. Nelder and C. Chatfield, The initial examination of data, Journal of the Royal Statistical Society, Series A, vol.148, pp.214-253, 1985.

H. Noçairi, C. Gomes, M. Thomas, and G. Saporta, Improving Stacking Methodology for Combining Classifiers; Applications to Cosmetic Industry, Electronic Journal of Applied Statistical Analysis, vol.9, pp.340-361, 2016.

C. O'neil, Weapons of Maths Destruction, 2016.

J. O. Ramsay and B. Silverman, Functional data analysis, 1997.

A. Rosipal and L. Trejo, Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space, Journal of Machine Learning Research, vol.2, pp.97-123, 2001.

B. Schölkopf, A. Smola, and K. L. Müller, Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation, vol.10, pp.1299-1319, 1998.

J. A. Suykens and J. Vandewalle, Least squares support vector machine classifiers, Neural Processing Letters, vol.9, pp.293-300, 1999.

G. Saporta, About maximal association criteria in linear analysis and in cluster analysis, in Classification and Related Methods of Data Analysis, Compstat Proceedings, Physica Verlag, pp.315-322, 1988.

G. Shmueli, To explain or to predict?, Statistical Science, vol.25, pp.289-310, 2010.

M. Tenenhaus, Analyse en composantes principales d'un ensemble de variables nominales ou numériques, Revue de Statistique Appliquée, vol.25, pp.39-56, 1977.

M. Tenenhaus, L'approche PLS, Revue de Statistique Appliquée, vol.17, pp.5-40, 1999.

A. Tenenhaus and M. Tenenhaus, Regularized Generalized Canonical Correlation Analysis, Psychometrika, vol.76, pp.257-284, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00554101

R. Tibshirani, Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society, Series B, vol.58, pp.267-288, 1996.

J. W. Tukey, The Future of Data Analysis, Ann. Math. Statist, vol.33, pp.1-67, 1962.

V. Vapnik, Estimation of Dependences Based on Empirical Data, vol.113, pp.7310-7315, 2006.

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

H. Zou, T. Hastie, and R. Tibshirani, Sparse Principal Component Analysis, Journal of Computational and Graphical Statistics, vol.15, pp.265-286, 2006.