Bayesian sparsity for statistical learning in high dimensions: applications to medical imaging

13 février 2020 / 13h


ISPED, université de Bordeaux
146 rue Léo Saignat, Bordeaux
Amphi Pierre-Alexandre Louis

entrée gratuite, sans inscription

 

Cet évènement du BPH, Bordeaux Population Health – UMR 1219 est organisé sous l’égide du Département Santé publique de l’université de Bordeaux.

 

Intervenant : Charles Bouveyron  (https://math.unice.fr/~cbouveyr/)

 

Résumé :

Although the ongoing digital revolution in fields such as chemometrics, genomics or personalized medicine gives hope for considerable progress in these areas, it also provides more and more high-dimensional data to analyze and interpret. A common usual task in those fields is discriminant analysis, which however may suffer from the high dimensionality of the data. The recent advances, through subspace classifica- tion or variable selection methods, allowed to reach either excellent classification performances or useful visualizations and interpretations. Obviously, it is of great interest to have both excellent classification accuracies and a meaningful variable selection for interpretation. This work addresses this issue by intro- ducing a subspace discriminant analysis method which performs a class-specific variable selection through Bayesian sparsity. The resulting classification methodology is called sparse high-dimensional discrimi- nant analysis (sHDDA). Contrary to most sparse methods which are based on the Lasso, sHDDA relies on a Bayesian modeling of the sparsity pattern and avoids the painstaking and sensitive cross-validation of the sparsity level. The main features of sHDDA are illustrated on simulated and real-world data. In particular, we propose an exemplar application to cancer characterization based on medical imaging using radiomic feature extraction is in particular proposed.
[F. Orlhac, P.-A. Mattei, C. Bouveyron and N. Ayache, Class-specific Variable Selection in High-Dimensional Discriminant Analysis through Bayesian Sparsity, Journal of Chemometrics, vol. 33, pp. e3097, 2019]
[C. Bouveyron, P. Latouche and P.-A. Mattei, Bayesian Variable Selection for Globally Sparse Probabilistic PCA, Electronic Journal of Statistics, vol. 12(2), pp. 3036-3070, 2018]



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