High-dimensional analysis of semidefinite relaxations for sparse principal components, IEEE International Symposium on, pp.2454-2458, 2008. ,
From predictive methods to missing data imputation: An optimization approach, Journal of Machine Learning Research, vol.18, issue.196, pp.1-39, 2018. ,
From multiblock partial least squares to multiblock redundancy analysis. a continuum approach, Informatica, vol.22, issue.1, pp.11-26, 2011. ,
mice: Multivariate imputation by chained equations in r, Journal of statistical software, pp.1-68, 2010. ,
Adaptive thresholding for sparse covariance matrix estimation, Journal of the American Statistical Association, vol.106, issue.494, pp.672-684, 2011. ,
Sparse partial least squares regression for simultaneous dimension reduction and variable selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.72, issue.1, pp.3-25, 2010. ,
Sparse discriminant analysis, Technometrics, vol.53, issue.4, pp.406-413, 2011. ,
A direct formulation for sparse pca using semidefinite programming, Advances in neural information processing systems, pp.41-48, 2005. ,
All-at-once Optimization for Coupled Matrix and Tensor Factorizations, 2011. ,
Understanding data fusion within the framework of coupled matrix and tensor factorizations. Chemometrics and Intelligent Laboratory Systems, vol.129, pp.53-63, 2013. ,
Adaptive regression and model selection in data mining problems, 1999. ,
Partial least squares for discrimination, Journal of Chemometrics: A Journal of the Chemometrics Society, vol.17, issue.3, pp.166-173, 2003. ,
Simultaneous clustering of gene expression data with clinical chemistry and pathological evaluations reveals phenotypic prototypes, BMC Systems Biology, vol.1, issue.1, p.15, 2007. ,
Sparse discriminant analysis, Technometrics, vol.53, issue.4, pp.406-413, 2011. ,
Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society: Series B (Methodological), vol.39, issue.1, pp.1-22, 1977. ,
An original methodology for the selection of biomarkers of tenderness in five different muscles, vol.8, p.206, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02429282
softImpute: Matrix Completion via Iterative Soft-Thresholded SVD. R package version, vol.1, 2015. ,
Handling missing values in exploratory multivariate data analysis methods, Journal de la Société Française de Statistique, vol.153, issue.2, pp.79-99, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00811888
missMDA: A Package for Handling Missing Values in Multivariate Data Analysis, Journal of Statistical Software, vol.70, issue.1, pp.1-31, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01314945
Gestion des données manquantes en analyse en composantes principales, Journal de la Société Française de Statistique, vol.150, issue.2, pp.28-51, 2009. ,
Linear regression and correlation, Mathematics of statistics, vol.1, pp.252-285, 1962. ,
A Sparse PLS for Variable Selection when Integrating Omics data, Statistical applications in genetics and molecular biology, vol.7, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-00300204
Group and sparse group partial least square approaches applied in genomics context, Bioinformatics, vol.32, issue.1, pp.35-42, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01288891
Highdimensional multi-block analysis of factors associated with thrombin generation potential, 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), pp.453-458, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02429302
Supervised learning for multi-block incomplete data, 2019. ,
Spectral regularization algorithms for learning large incomplete matrices, Journal of machine learning research, vol.11, pp.2287-2322, 2010. ,
Inconsistent systems of linear equations. The Mathematical Gazette, vol.63, pp.181-185, 1979. ,
, The R Journal, vol.20
A Unifying Tool for Linear Multivariate Statistical Methods: The RV-Coefficient, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.25, issue.3, pp.257-265, 1976. ,
Inference and missing data, Biometrika, vol.63, issue.3, pp.581-592, 1976. ,
Missing data: our view of the state of the art, Psychological methods, vol.7, issue.2, p.147, 2002. ,
A Sparse-Group Lasso, Journal of Computational and Graphical Statistics, vol.22, issue.2, pp.231-245, 2013. ,
MissForest--non-parametric missing value imputation for mixedtype data, Bioinformatics, vol.28, issue.1, pp.112-118, 2011. ,
Regularized generalized canonical correlation analysis, Psychometrika, vol.76, issue.2, p.257, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00554101
Variable selection for generalized canonical correlation analysis, Biostatistics, vol.15, issue.3, pp.569-583, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01071432
Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996. ,
A multiblock partial least squares algorithm for investigating complex chemical systems, Journal of chemometrics, vol.3, issue.1, pp.3-20, 1989. ,
Hierarchical grouping to optimize an objective function, Journal of the American statistical association, vol.58, issue.301, pp.236-244, 1963. ,
Solutions of ill-posed problems (an tikhonov and vy arsenin), SIAM Review, vol.21, issue.2, p.266, 1979. ,
A meta-analysis of genome-wide association studies identifies orm1 as a novel gene controlling thrombin generation potential, Blood, vol.123, issue.5, pp.777-785, 2014. ,
Thrombin generation potential and whole-blood dna methylation, Thrombosis research, vol.135, issue.3, pp.561-564, 2015. ,
Highly multiplexed antibody suspension bead arrays for plasma protein profiling, The Low Molecular Weight Proteome, pp.137-145, 2013. ,
Genome wide association study for plasma levels of natural anticoagulant inhibitors and protein c anticoagulant pathway: the martha project, British journal of haematology, vol.157, issue.2, pp.230-239, 2012. ,
A sparse pls for variable selection when integrating omics data, Statistical applications in genetics and molecular biology, vol.7, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-00300204
Matrix completion and low-rank svd via fast alternating least squares, The Journal of Machine Learning Research, vol.16, issue.1, pp.3367-3402, 2015. ,
missmda: a package for handling missing values in multivariate data analysis, Journal of Statistical Software, vol.70, issue.1, pp.1-31, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01314945
The nature of statistical learning theory, Springer science & business media, 2013. ,
Random forests, Machine learning, vol.45, issue.1, pp.5-32, 2001. ,
Supervised learning for multiblock incomplete data, 2019. ,
C4bpb/c4bpa is a new susceptibility locus for venous thrombosis with unknown protein s independent mechanism: results from genome-wide association and gene expression analyses followed by case-control studies, Blood, p.2010, 2010. ,
Relationship between beef consumer tenderness perception and Warner-Bratzler shear force, Meat Sci, vol.78, pp.153-156, 2008. ,
Relationships of consumer sensory ratings, marbling score, and shear force value to consumer acceptance of beef strip loin steaks, J. Anim. Sci, vol.81, pp.2741-2750, 2003. ,
Expression marker-based strategy to improve beef quality, Sci. World J, 2016. ,
Recent advances in omic technologies for meat quality management, Meat sci, vol.109, pp.18-26, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01594439
Proteomic Investigations of Beef Tenderness, Proteomics in Food Science, pp.177-197, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01580641
Supervised Learning for Multi-Block Incomplete Data, p.14, 2019. ,
High-dimensional multi-block analysis of factors associated with thrombin generation potential, Proceedings of the 32th International Symposium on Computer-Based Medical Systems, pp.5-7, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02429302
Warner-Bratzler shear evaluations of 40 bovine muscles, Meat Sci, vol.64, pp.507-512, 2003. ,
The relationship between beef tenderness and age classification of beef carcasses in South Africa, Proceedings of the 8th Meat Symposium: Meat, Le Needs of the South Africaner Consumer, pp.57-66, 1995. ,
Genetic and environmental effects on meat quality, Meat Sci, vol.86, pp.171-183, 2010. ,
Can postmortem proteolysis explain tenderness differences in various bovine muscles?, Meat Sci, vol.137, pp.114-122, 2018. ,
Variation in palatability and biochemical traits within and among eleven beef muscles, J. Anim. Sci, vol.82, pp.534-550, 2004. ,
Mapping of bovine skeletal muscle proteins using two-dimensional gel electrophoresis and mass spectrometry, Proteomics, vol.4, pp.1811-1824, 2004. ,
A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding, Anal. Biochem, vol.72, pp.248-254, 1976. ,
Validation of a dot-blot quantitative technique for large scale analysis of beef tenderness biomarkers, J. Physiol. Pharmacol, vol.60, pp.91-97, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-01211921
Functional analysis of beef tenderness, J. Proteom, vol.75, pp.352-365, 2011. ,
Protocol for high-resolution electrophoresis separation of myosin heavy chain isoforms in bovine skeletal muscle, Electrophoresis, vol.32, pp.1804-1806, 2011. ,
Electrophoretic separation of rat skeletal muscle myosin heavy-chain isoforms, J. Appl. Physiol, vol.75, pp.2337-2340, 1993. ,
Electrophoretic separation of bovine muscle myosin heavy chain isoforms, Meat Sci, vol.53, pp.1-7, 1999. ,
Sensory Analysis-General Guidance for the Staff of a Sensory Evaluation Laboratory; International Organization for Standardization, 2006. ,
Technical Note: Use of belt grill cookery and slice shear force for assessment of pork longissimus tenderness1, J. Anim. Sci, vol.82, pp.238-241, 2004. ,
, The R Project for Statistical Computing, 2019.
A new insight into the role of calpains in post-mortem meat tenderization in domestic animals: A review. Asian Aus, J. Anim. Sci, vol.26, 2013. ,
Very early activation of m-calpain in peripheral nerve during Wallerian degeneration, J. Neurol. Sci, vol.196, pp.9-20, 2002. ,
Biochemical factors regulating the toughening and tenderization processes of meat, Meat Sci, vol.43, pp.193-201, 1996. ,
Tenderness-An enzymatic view, Meat Sci, vol.84, pp.248-256, 2010. ,
Postmortem proteolysis is reduced in transgenic mice overexpressing calpastatin, J. Anim. Sci, vol.82, pp.794-801, 2004. ,
Different phenotypic and proteomic markers explain variability of beef tenderness across muscles, Int. J. Biol, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-01000621
Inverse relationships between biomarkers and beef tenderness according to contractile and metabolic properties of the muscle, J. Agric. Food Chem, vol.62, pp.9808-9818, 2014. ,
Protein degradation and protection against misfolded or damaged proteins, Nature, vol.426, 2003. ,
New indicators of beef sensory quality revealed by expression of specific genes, J. Agric. Food Chem, vol.55, pp.5229-5237, 2007. ,
Variations in the abundance of 24 protein biomarkers of beef tenderness according to muscle and animal type, Animal, vol.5, pp.885-894, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-01000706
A critical role for histone H2AX in recruitment of repair factors to nuclear foci after DNA damage, Curr. Biol, vol.10, pp.886-895, 2000. ,
MDC1 is a mediator of the mammalian DNA damage checkpoint, Nature, vol.421, p.961, 2003. ,
?-H2AX as a therapeutic target for improving the efficacy of radiation therapy, Curr. Cancer Drug Targets, vol.6, pp.197-205, 2006. ,
, Functional roles and potential applications, vol.118, pp.683-692, 2009.
, kernelPCA ACP à noyaux -ou kernelPCA, vol.8, p.168, 2001.
, Correspond à la réduction de la norme de l'estimateur. Réalisé dans le spectre par les estimateurs Ridge et PCR et en norme L 1 pour le Lasso, par exemple, vol.29, p.168
, ACP Analyse en Composantes Principales -ou PCA pour Principal Component Analysis, vol.50, p.181, 1933.
, AFM Analyse Factorielle Multiple (Escofier et collab, vol.49, p.168, 1984.
, AIC Critère d'Information d'Akaike -ou Akaike Information Criterion, vol.39, p.168, 1973.
, aLasso Lasso adaptatif, vol.35, p.168, 2006.
Imputation Simple par Alternance -ou Alternate Simple-Imputation, vol.57, p.168, 2006. ,
Information Bayésienne -ou Bayesian Information Criterion (Schwarz et collab, vol.39, p.168, 1978. ,
Linéaire sans Biais -ou Best Linear Unbiased Estimator (Pearson, 1901), vol.18, p.168 ,
, CBMS Computer-Based Medical Systems, p.168
, CCA Analyse Canonique des Corrélations -ou Canonical Correlation Analysis (Hotelling, 1936), vol.48, p.168
, CMTF Factorisation Tensorielle pour une Collection de Matrices -ou Collective Matrix Tensor Factorization (Acar et collab, vol.51, p.168, 2011.
Factorisation ALternée Tensorielle pour une Collection de Matrices -ou Alternate Collective Matrix Tensor Factorization (Acar et collab, p.168, 2013. ,
Factorisation Tensorielle Optimale pour une Collection de Matrices -ou Optimal Collective Matrix Tensor Factorization (Acar et collab, p.168, 2013. ,
Factorisation Tensorielle Optimale Pondérée pour une Collection de Matrices -ou Weighted Optimal Collective Matrix Tensor Factorization (Acar et collab, vol.57, p.168, 2013. ,
, Optimale Pondérée -ou Weighted Optimal PARAFAC (Acar et collab, vol.57, p.168, 2011.
, CRAN Comprehensive R Archival Network Miscellaneous (R Core Team, vol.33, issue.3, p.168, 2019.
, , vol.36, p.168
, DCCA Analyse Canonique Profonde des Corrélations -ou Deep Canonical Correlation Analysis, p.168
, ddsPLS data-driven sparse PLS (Lorenzo et collab., 2019b). vii, vol.3, pp.165-168
, dGN Gauss-Newton amorti -ou damped Gauss, vol.168
, Elastic-net Réseau Élastique -ou Elastic Network, méthode de régularisation, vol.26, p.168, 2005.
, Golub et collab, vol.43, p.168, 1979.
, gPLS PLS groupée -ou group PLS (Liquet et collab, vol.33, p.168, 2015.
, INDAFAC paraFAC pour Données INcomplètes -ou INcomplete DAta paraFAC (Tomasi et, vol.57, p.168, 2005.
, , vol.56, p.168, 2012.
, LARS Régression à Corrélations Minimales Hiérarchique -ou Least Angle Regression Stagewise (Jolliffe et collab, vol.22, p.168, 2003.
, Lasso Least Absolute Shrinkage and Selection Operator, vol.60, p.186, 1996.
, MBPLS Moindres Carrés Partiels Multi Blocs -ou Multiblocks pls, vol.47, p.168, 1989.
, MCO Critère des Moindres Carrés Ordinaires -ou OLS pour Ordinary Least Squares criterion, vol.1, p.186
, MMMF Factorisation de Matrice à Marge Maximale -ou Maximum-Margin Matrix Factorization (Hastie et collab, vol.55, p.168, 2015.
, MSE Erreur Quadratique Moyenne -ou Mean Squared Error, vol.168
, multibloc Données formées par plusieurs groupes de variables ne partagean pas forcemment la même structure ni la même nature. 2, 3, 8, vol.33, p.173
, multivoie Données formées par plusieurs modalités d'un même ensemble de variables, vol.167, p.168
, NIPALS PLS Itérative Non linéaire -ou Nonlinear Iterative Partial Least Squares, vol.32, p.180, 1989.
, , vol.50, p.168, 1996.
Analyse Factorielle PARAallèle -ou PARAllel FACtor analysis, aussi appelée CandecomP (CP) (Harshman, 1970), vol.57, p.168 ,
, PCR Régression sur Composantes Principales (Hotelling, 1957), vol.33, p.169
, PLASM Probing Least Absolute Squares Modelling, vol.26, p.168, 1999.
, PLS Moindres Carrés Partiels -ou Partial Least Squares, vol.2, p.168, 1983.
, , vol.63, p.168, 2003.
, , vol.60, p.168, 2013.
, PPCA Probabilist ACP (PPCA) (Caussinus, p.168, 1986.
, PRESS Somme des Carrés Résiduels de l'Erreur en Prédiction -ou Predictive Residual Error Sum of Squares, vol.58, p.168
, RDA Analyse des Redondances -ou Redundancy Analysis (van den Wollenberg, vol.48, p.168, 1977.
, RGCCA Analyse Canonique des Corrélations Régularisée Généralisée -ou Regularized Generalized Canonical Correlation Analysis (Tenenhaus et Tenenhaus, vol.48, p.168, 2011.
, Ridge Principe de régularisation limitant à un espace atteignable par les paramètres du problème à une hyperboule de norme 2 et de rayon le paramètre ? (voir Hoerl et Kennard, vol.173, p.185, 1970.
, RMSE Erreur Quadratique Moyenne sous Racine-ou Root Mean Squared Error, p.168
, SCAD Smoothly Clipped Absolute Deviation Penalty, vol.35, p.168, 2009.
, SCE Somme des Carrés Expliqués, vol.37, p.168
, SCoTLASS Simplification de Méthode à Composantes par Lasso -ou Simplified Component Technique-Lasso, vol.33, p.168, 2003.
, SCR Somme des Carrés des Résidus, vol.37, p.168
, SDP Programmation Positive -ou Semi-Definite Programming (d'Aspremont et collab, vol.36, p.168, 2005.
, SE Erreur Quadratique -ou Squared Error, p.168
, SGCCA Analyse Canonique des Corrélations Généralisée Parcimonieuse -ou Sparse Generalized Canonical Correlation Analysis (Tenenhaus et collab, vol.48, p.168, 2014.
, sgPLS PLS parcimonieuse groupée -ou sparse group PLS (Liquet et collab, vol.33, p.168, 2015.
, sparseACP Analyse en Composantes Principales Parcimonieuse -ou sparse PCA, vol.34, p.168, 2004.
, Acronymes SPCA Sensible Principal Component Analysis (SPCA), p.168
, sPLS PLS parcimonieuse -ou sparse PLS (Lê Cao et collab, vol.30, p.168, 2008.
, SRM Minimisation du Risque Structurel -ou Structural Risk Minimization (Vapnik et Chervonenkis, vol.10, p.168, 2015.
, STATIS Structuration de Tableaux À Trois Indices de la Statistique (L'Hermier des Plantes, vol.49, p.168, 1976.
, SVD Décomposition en Valeurs Singulières -ou Singular Value Decomposition, vol.17, p.178, 1901.
, SVM Machine à Vecteur de Support -ou Support Vector Machine, 1995.
, Tucker3 Three-mode principal component model (Tucker3) (Kroonenberg, 1983), vol.50, p.168
, VC Dimension de Vapnik-Chervonenkis, vol.168
The monoblock to multiblock differences of explained variances" where a positive value, respectively negative value, means monoblock, respectively multiblock, performs better. Parameters are p 1 " p 2 " 50, through 100 simulations per set of parameters for q 1 P rr1, 10ss, and q 2 " 20´q 1 and n " 100, The norm of differences of regression matrices" in bottom left corner and filled in red : the higher it is the more different are the estimated regression matrices from both of the approaches, vol.20 ,
, Soient f et g 1 ,..,g p des fonctions de classe C 1 sur un ouvert U de R n , à valeurs dans R et X l'ensemble défini par X " x P U|g 1 pxq
, Si la restriction de f à X admet un extremum local en a et si les différentielles dg 1 paq, . . . , dg p paq sont des formes linéaires indépendantes, alors il existe des réels uniques ? 1, p.que
, Les coefficients ? i sont appelés les multiplicateurs de Lagrange et on appelle "lagrangien" la fonction Lpx
Théorème précédent on dit que les contraintes sont actives lorsque le point critique considéré est bien dans X ,
, Pseudo-inverses On remarque que pour toute matrice inversible A, la matrice A´1 est aussi pseudo-inverse de A. Il est possible de montrer que l'ensemble des pseudo-inverses de A est infini
Soit une matrice réelle A P R n?p quelconque, on appelle matrice pseudo-inverse de A toute matrice B P R n?n qui vérifie ABA " A ,
, Cette définition permet aussi d'introduire la pseudo-inverse de Moore-Penrose, qui est un cas particulier de pseudo-inverse 178
Notions mathématiques ,
, Alors la pseudo inverse de Moore-Penrose de A, notée A, 1956.
, Il est possible de démontrer que pour toute matrice réelle, sa pseudo-inverse de Moore-Penrose existe et est unique (voir par exemple Rao et collab, 1972.
, Décomposition SVD Afin de déterminer la forme de la pseudo-inverse de Moore-Penrose, il convient d
«A scalable optimization approach for fitting canonical tensor decompositions, Journal of Chemometrics, vol.25, issue.2, pp.67-86, 2011. ,
«All-at-once optimization for coupled matrix and tensor factorizations, 2011. ,
«Understanding data fusion within the framework of coupled matrix and tensor factorizations, vol.129, pp.53-63, 2013. ,
«Information theory and an extension of the maximum likelihood principle, dans 2nd International Symposium on Information Theory, pp.267-281, 1973. ,
«The relationship between variable selection and data agumentation and a method for prediction, Technometrics, vol.16, issue.1, pp.125-127, 1974. ,
, , 2018.
Adaptive regression and model selection in data mining problems, 1999. ,
«Partial least squares for discrimination, Journal of Chemometrics : A Journal of the Chemometrics Society, vol.17, issue.3, pp.166-173, 2003. ,
Newell. 2019, «Dimensionality reduction for visualizing single-cell data using umap», Nature biotechnology, vol.37, issue.1, p.38 ,
«Regularized estimation of large covariance matrices, The Annals of Statistics, vol.36, issue.1, pp.199-227, 2008. ,
From multiblock partial least squares to multiblock redundancy analysis, Informatica, vol.22, issue.1, pp.11-26, 2011. ,
«Bagging predictors, Machine learning, vol.24, issue.2, pp.123-140, 1996. ,
, Machine learning, vol.45, issue.1, pp.5-32, 2001.
«Submodel selection and evaluation in regression. the x-random case, International statistical review/revue internationale de Statistique, pp.291-319, 1992. ,
«Multiway calibration. multilinear pls», Journal of chemometrics, vol.10, issue.1, pp.47-61, 1996. ,
«Analysis of individual differences in multidimensional scaling via an n-way generalization of "eckart-young" decomposition, Psychometrika, vol.35, issue.3, pp.283-319, 1970. ,
«Models and uses of principal component analysis, Multidimensional data analysis, vol.86, pp.149-170, 1986. ,
«The sem algorithm : a probabilistic teacher algorithm derived from the em algorithm for the mixture problem, Computational statistics quarterly, vol.2, pp.73-82, 1985. ,
«Sparse partial least squares regression for simultaneous dimension reduction and variable selection», Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.72, issue.1, pp.3-25, 2010. ,
, Sparse discriminant analysis, vol.53, pp.406-413, 2011.
, Apprentissage artificiel : Concepts et algorithmes, 2010.
, Machine learning, vol.20, issue.3, pp.273-297, 1995.
«A direct formulation for sparse pca using semidefinite programming», dans Advances in neural information processing systems, pp.41-48, 2005. ,
«Pls shrinks, Journal of chemometrics, vol.9, issue.4, pp.323-326, 1995. ,
«Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society : Series B (Methodological), vol.39, issue.1, pp.1-22, 1977. ,
, Sparse pca via covariance thresholding, vol.17, pp.1-41, 2016.
«A unified bias-variance decomposition, dans Proceedings of 17th International Conference on Machine Learning, pp.231-238, 2000. ,
, Rcpp : Seamless R and C++ integration, vol.40, pp.1-18, 2011.
Estimating the error rate of a prediction rule : improvement on crossvalidation», Journal of the American statistical association, vol.78, pp.316-331, 1983. ,
, Least angle regression, vol.32, pp.407-499, 2004.
«L'analyse factorielle multiple, Cahiers du Bureau universitaire de recherche opérationnelle Série Recherche, vol.42, pp.3-68, 1984. ,
, Comments on «wavelets in statistics : A review, vol.6, p.131, 1997.
«The predictive sample reuse method with applications, Journal of the American statistical Association, vol.70, pp.320-328, 1975. ,
, Neural networks and the bias/variance dilemma», vol.4, pp.1-58, 1992.
«Variable selection using random forests, Pattern Recognition Letters, vol.31, pp.2225-2236, 2010. ,
«Vsurf : an r package for variable selection using random forests, The R Journal, 2015. ,
«Evaluation of multiple-imputation procedures for three-mode component models, Journal of Statistical Computation and Simulation, vol.87, pp.3059-3081, 2017. ,
«Generalized cross-validation as a method for choosing a good ridge parameter, Technometrics, vol.21, issue.2, pp.215-223, 1979. ,
«Multi-way pls regression : Monotony convergence of tri-linear pls2 and optimality of parameters, Computational Statistics & Data Analysis, vol.83, pp.129-139, 2015. ,
«Foundations of the parafac procedure : Models and conditions for an "explanatory" multi-modal factor analysis, UCLA Working Papers in Phonetics, vol.16, 1970. ,
«Matrix completion and low-rank svd via fast alternating least squares, Journal of Machine Learning Research, vol.16, issue.1, pp.3367-3402, 2015. ,
«Ridge regression : Biased estimation for nonorthogonal problems, Technometrics, vol.12, issue.1, pp.55-67, 1970. ,
Analysis of a complex of statistical variables into principal components.», Journal of educational psychology, vol.24, issue.6, p.417, 1933. ,
«Relations between two sets of variates, Biometrika, vol.28, pp.321-377, 1936. ,
«The relations of the newer multivariate statistical methods to factor analysis, British Journal of Statistical Psychology, vol.10, issue.2, pp.69-79, 1957. ,
Van Kerckhoven et T. Verdonck. 2012, «Robust parafac for incomplete data, Journal of Chemometrics, vol.26, issue.6, pp.290-298 ,
«Handling missing values in multiple factor analysis, Food quality and preference, vol.30, issue.2, pp.77-85, 2013. ,
, Sur les fonctions convexes et les inégalités entre les valeurs moyennes, vol.30, pp.175-193, 1906.
, Sparse principal components analysis», Unpublished manuscript, vol.7, 2004.
«On consistency and sparsity for principal components analysis in high dimensions, Journal of the American Statistical Association, vol.104, pp.682-693, 2009. ,
«A modified principal component technique based on the lasso, Journal of computational and Graphical Statistics, vol.12, issue.3, pp.531-547, 2003. ,
«Handling missing values in exploratory multivariate data analysis methods», Journal de la Société Française de Statistique, vol.153, issue.2, pp.79-99, 2012. ,
«missmda : a package for handling missing values in multivariate data analysis, Journal of Statistical Software, vol.70, issue.1, pp.1-31, 2016. ,
«Gestion des données manquantes en analyse en composantes principales», Journal de la Société Française de Statistique, vol.150, issue.2, pp.28-51, 2009. ,
«Weighted least squares fitting using ordinary least squares algorithms, Psychometrika, vol.62, issue.2, pp.251-266, 1997. ,
, Sparsity in penalized empirical risk minimization», dans Annales de l'IHP Probabilités et statistiques, vol.45, pp.7-57, 2009.
«Do semidefinite relaxations solve sparse pca up to the information limit ?, The Annals of Statistics, vol.43, issue.3, pp.1300-1322, 2015. ,
Three-mode principal component analysis : Theory and applications, vol.2, 1983. ,
Three-way arrays : rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics, Linear algebra and its applications, vol.18, issue.2, pp.95-138, 1977. ,
«integromics : an r package to unravel relationships between two omics data sets, Bioinformatics, vol.25, pp.2855-2856, 2009. ,
«A sparse pls for variable selection when integrating omics data, Statistical applications in genetics and molecular biology, vol.7, p.1, 2008. ,
«Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, pp.2278-2324, 1998. ,
,
Group and sparse group partial least square approaches applied in genomics context, Bioinformatics, vol.32, issue.1, pp.35-42, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01288891
Latent variable path modeling with partial least squares, 2013. ,
«An original methodology for the selection of biomarkers of tenderness in five different muscles, vol.8, p.206, 2019. ,
«Supervised learning for multi-block incomplete data, 2019. ,
«Cross-validation of multiway component models, Journal of Chemometrics : A Journal of the Chemometrics Society, vol.13, issue.5, pp.491-510, 1999. ,
«Visualizing data using t-sne», Journal of machine learning research, vol.9, pp.2579-2605, 2008. ,
, Technometrics, vol.15, issue.4, pp.661-675, 1973.
«An algorithm for least-squares estimation of nonlinear parameters, Journal of the society for Industrial and Applied Mathematics, vol.11, issue.2, pp.431-441, 1963. ,
«Spectral regularization algorithms for learning large incomplete matrices, Journal of machine learning research, vol.11, pp.2287-2322, 2010. ,
, Gradient methods for minimizing composite functions», Mathematical Programming, vol.140, pp.125-161, 2013.
«Selection of optimal regression models via cross-validation», Journal of Chemometrics, vol.2, issue.1, pp.39-48, 1988. ,
On lines and planes of closest fit to systems of point in space, Philosophical Magazine, vol.2, pp.559-572, 1901. ,
«On best approximate solutions of linear matrix equations, dans Mathematical Proceedings of the Cambridge Philosophical Society, vol.52, pp.17-19, 1956. ,
, Structuration des tableaux à trois indices de la statistique, 1976.
«On unifying multiblock analysis with application to decentralized process monitoring, Journal of chemometrics, vol.15, issue.9, pp.715-742, 2001. ,
, R : A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, 2019.
«Generalized inverse of a matrix and its applications, dans Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Theory of Statistics, vol.1, pp.601-620, 1972. ,
«Fast maximum margin matrix factorization for collaborative prediction, dans Proceedings of the 22nd international conference on Machine learning, pp.713-719, 2005. ,
«Boosting as a regularized path to a maximum margin classifier, Journal of Machine Learning Research, vol.5, pp.941-973, 2004. ,
«Generalized thresholding of large covariance matrices», Journal of the American Statistical Association, vol.104, pp.177-186, 2009. ,
«Em algorithms for pca and spca», dans Advances in neural information processing systems, pp.626-632, 1998. ,
«Inference and missing data, Biometrika, vol.63, issue.3, pp.581-592, 1976. ,
«Missing data : our view of the state of the art, Psychological methods, vol.7, issue.2, p.147, 2002. ,
Learning with kernels : support vector machines, regularization, optimization, and beyond, 2001. ,
«Estimating the dimension of a model, The annals of statistics, vol.6, pp.461-464, 1978. ,
«A sparse-group lasso, Journal of Computational and Graphical Statistics, vol.22, issue.2, pp.231-245, 2013. ,
«Multiway multiblock component and covariates regression models, Journal of Chemometrics : A Journal of the Chemometrics Society, vol.14, issue.3, pp.301-331, 2000. ,
Missforest-non-parametric missing value imputation for mixed-type data, Bioinformatics, vol.28, issue.1, pp.112-118, 2011. ,
«Cross-validatory choice and assessment of statistical predictions, Journal of the Royal Statistical Society : Series B (Methodological), vol.36, issue.2, pp.111-133, 1974. ,
Grill et V. Frouin. 2014, «Variable selection for generalized canonical correlation analysis, Biostatistics, vol.15, issue.3, pp.569-583 ,
Regularized generalized canonical correlation analysis, Psychometrika, vol.76, issue.2, p.257, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00554101
«Regression shrinkage and selection via the lasso», Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996. ,
, , p.203
, On the stability of inverse problems, vol.39, pp.195-198, 1943.
«Probabilistic principal component analysis, Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.61, issue.3, pp.611-622, 1999. ,
, Chemometrics and Intelligent Laboratory Systems, vol.75, pp.163-180, 2005.
«A comparison of algorithms for fitting the parafac model, Computational Statistics & Data Analysis, vol.50, issue.7, pp.1700-1734, 2006. ,
«Principles of risk minimization for learning theory», Advances in neural information processing systems, pp.831-838, 1992. ,
«Statistical learning theory, 1998. ,
, The Nature of Statistical Learning Theory, 1999.
«An overview of statistical learning theory, IEEE transactions on neural networks, vol.10, issue.5, pp.988-999, 1999. ,
On the uniform convergence of relative frequencies of events to their probabilities, pp.11-30, 2015. ,
, Modern Applied Statistics with S, 4 e éd, 2002.
1838, «Notice sur la loi que la population suit dans son accroissement, Corresp. Math. Phys, vol.10, pp.113-126 ,
«A multiblock partial least squares algorithm for investigating complex chemical systems, Journal of chemometrics, vol.3, issue.1, pp.3-20, 1989. ,
«Multivariate modelling of the tablet manufacturing process with wet granulation for tablet optimization and in-process control, International journal of Pharmaceutics, vol.156, issue.1, pp.109-117, 1997. ,
«Deflation in multiblock pls, Journal of chemometrics, vol.15, issue.5, pp.485-493, 2001. ,
, Three pls algorithms according to sw», dans Proc. : Symposium MULDAST (multivariate analysis in science and technology), pp.26-30, 1984.
Kettaneh-Wold et B. Skagerberg. 1989, «Nonlinear pls modeling, Chemometrics and intelligent laboratory systems, vol.7, pp.53-65 ,
«The multivariate calibration problem in chemistry solved by the pls method», dans Matrix pencils, pp.286-293, 1983. ,
«Pls-regression : a basic tool of chemometrics, Chemometrics and intelligent laboratory systems, vol.58, pp.109-130, 2001. ,
, Analyse supervisée multibloc en grande dimension 204 BIBLIOGRAPHIE van den Wollenberg, A. L. 1977, «Redundancy analysis an alternative for canonical correlation analysis, Psychometrika, vol.42, issue.2, pp.207-219
«Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.68, issue.1, pp.49-67, 2006. ,
«On model selection consistency of lasso, Journal of Machine learning research, vol.7, pp.2541-2563, 2006. ,
«The adaptive lasso and its oracle properties, Journal of the American statistical association, vol.101, pp.1418-1429, 2006. ,
«Regularization and variable selection via the elastic net, Journal of the royal statistical society : series B (statistical methodology), vol.67, pp.301-320, 2005. ,
«Sparse principal component analysis, Journal of computational and graphical statistics, vol.15, issue.2, pp.265-286, 2006. ,