Stéphane Girard

Stéphane Girard
Senior research scientist at Inria Grenoble Rhône-Alpes.

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Extremes

High dimensional statistical learning

Functional estimation

Applications

MxM

p=M²

Figure 1: Dataset (projected into a 3-dimensional space for visualization purposes) obtained by extracting some local descriptors on a rotating object. Different colors correspond to different scales. The dataset is highly non-linear.

nonlinear dimension reduction methods

high-dimensional data classifiers

Figure 2: Application of HDDA to Optical Character Recognition. This demonstration was realized by Charles Bouveyron. See the paragraph 5.2.2 of his PhD thesis for more details. The dataset is available here.

Copula

high dimensional regression

SIR method

Functional estimation

PhD theseshere

M. Allouche, "Contributions to generative modeling and dictionary learning: Theory and application", Institut Polytechnique de Paris, décembre 2022 [pdf].

A. Constantin, "Analyse de séries temporelles massives d'images satellitaires: Applications à la cartographie des écosystèmes", Université Grenoble Alpes, décembre 2021 [pdf].

M. Lopes, "Suivi écologique des prairies semi-naturelles : analyse statistique de séries temporelles denses d'images satellite à haute résolution spatiale", Université de Toulouse, novembre 2017 [pdf].

S. N. Sylla, "Modélisation et classification de données binaires en grande dimension - Application à l'autopsie verbale", Université Gaston Berger, Sénégal, décembre 2016 [pdf].

A. Chiancone, "Réduction de dimension via Sliced Inverse Regression: Idées et nouvelles propositions", Université Grenoble Alpes, octobre 2016 [pdf].

G. Mazo, "Construction et estimation de copules en grande dimension", Université Grenoble 1, novembre 2014 [pdf].

C. Bouveyron, "Modélisation et classification des données de grande dimension. Application à l'analyse d'images", Université Grenoble 1, septembre 2006 [pdf].

Classification of high-dimensional data

A. Constantin, M. Fauvel & S. Girard, "Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series", Statistics and Computing, 32, 79, 2022. [Associated technical report: pdf].

A. Constantin, M. Fauvel & S. Girard, "Joint supervised classification and reconstruction of irregularly sampled satellite image times series", IEEE Transactions on Geoscience and Remote Sensing, 60, 1--13, 2022. [Associated technical report: pdf].

M. Lopes, M. Fauvel, A. Ouin & S. Girard. "Spectro-temporal heterogeneity measures from dense high spatial resolution satellite image time series: Application to grassland species diversity estimation", Remote Sensing, 9 (10), 2017. [Associated technical report: pdf].

M. Lopes, M. Fauvel, S. Girard & D. Sheeren. "Object-based classification from high resolution satellite image time series with Gaussian mean map kernels: Application to grassland management practices", Remote Sensing, 9 (7), 2017. [Associated technical report: pdf].

S. Girard and J. Saracco. "Supervised and unsupervised classification using mixture models" In D. Fraix-Burnet and S. Girard, editors, Statistics for astrophysics, clustering and classification, volume 77, pages 69--90, EDP Sciences, 2016. [Associated technical report: pdf].

S. Sylla, S. Girard, A. Diongue, A. Diallo & C. Sokhna. A classification method for binary predictors combining similarity measures and mixture models", Dependence Modeling, 3, 240--255, 2015. [Associated technical report: pdf].

M. Fauvel, C. Bouveyron and S. Girard, "Parsimonious Gaussian process models for the classification of hyperspectral remote sensing images", IEEE Geoscience and Remote Sensing Letters, 12, 2423--2427, 2015. [Associated technical report: pdf].

C. Bouveyron, M. Fauvel & S. Girard. "Kernel discriminant analysis and clustering with parsimonious Gaussian process models", Statistics and Computing, 25, 1143--1162, 2015. [Associated technical report: pdf].

L. Bergé, C. Bouveyron & S. Girard. "HDclassif: An R package for model-based clustering and discriminant analysis of high-dimensional data, Journal of Statistical Software, 46(6), 1--29, 2012. [Associated technical report: pdf].

C. Bouveyron & S. Girard. "Robust supervised classification with mixture models: Learning from data with uncertain labels", Pattern Recognition, 42(11), 2649--2658, 2009. [Associated technical report: pdf].

C. Bouveyron & S. Girard. "Classification supervisée et non supervisée des données de grande dimension, La revue Modulad, 40, 81--102, 2009.

C. Bouveyron, S. Girard & C. Schmid. "High-dimensional discriminant Analysis", Communication in Statistics - Theory and Methods, 36(14), 2607--2623, 2007. [Associated technical report (in french): ps/pdf].

C. Bouveyron, S. Girard & C. Schmid. "High Dimensional Data Clustering", Computational Statistics and Data Analysis, 52, 502--519, 2007. [Associated technical report: ps/pdf].

C. Bouveyron, S. Girard & C. Schmid. "Class-specific subspace discriminant analysis for high-dimensional data", In C. Saunders et al., editors, Lecture Notes in Computer Science, volume 3940, p. 139-150. Springer-Verlag, Berlin Heidelberg, 2006.

Dimension reduction with nonlinear Principal Component Analysis

S. Girard & S. Iovleff. "Auto-associative models, nonlinear Principal component analysis, manifolds and projection pursuit" In A. Gorban et al, editors, Principal Manifolds for Data Visualisation and Dimension Reduction, volume 28, p. 205-222, LNCSE, Springer-Verlag, 2007. [Associated technical report: ps/pdf].

S. Girard & S. Iovleff. "Auto-Associative Models and Generalized Principal Component Analysis", Journal of Multivariate Analysis, 93(1):21-39, 2005. [Associated technical report: ps/pdf].

S. Girard."A nonlinear PCA based on manifold approximation", Computational Statistics, 15(2):145-167, 2000. [Associated technical report: pdf].

B. Chalmond & S. Girard. "Nonlinear modeling of scattered multivariate data and its application to shape change", IEEE Pattern Analysis and Machine Intelligence, 21(5):422-432, 1999. [Associated technical report: pdf].

S. Girard, B. Chalmond & J-M. Dinten. "Position of principal component analysis among auto-associative composite models", Comptes-Rendus de l'Académie des Sciences, t. 326, Série I:763-768, 1998.

S. Girard, B. Chalmond & J-M. Dinten. "Une ACP non-linéaire basée sur l'approximation par variétés", Revue de Statistique Appliquée, XLVI(3):5-19, 1998.

S. Girard, B. Chalmond & J-M. Dinten. "Designing non linear models for flexible curves", Curves and Surfaces with Application in CAGD, A. Le Méhauté, C. Rabut, and L.L. Schumaker (eds.), 135-142, 1997.