%0 Book Section
%9 OS CH : Chapitres d'ouvrages scientifiques
%A Sokolovska, N.
%A Nguyen Thanh Hai
%A Clément, K.
%A Zucker, Jean-Daniel
%T Deep Self-Organising Maps for efficient heterogeneous biomedical signatures extraction
%B Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN)
%C Piscataway
%D 2016
%L fdi:010069089
%G ENG
%I IEEE
%@ 978-1-5090-0619-9
%P 5079-5086
%R 10.1109/IJCNN.2016.7727869
%U https://www.documentation.ird.fr/hor/fdi:010069089
%> https://www.documentation.ird.fr/intranet/publi/depot/2017-02-01/010069089.pdf
%W Horizon (IRD)
%X Feature selection is used to preserve significant properties of data in a compact space. In particular, feature selection is needed in applications, where information comes from multiple heterogeneous high dimensional sources. Data integration, however, is a challenge in itself. In our contribution, we introduce a feature selection framework based on powerful visualisation capabilities of self-organising maps, where the deep structure can be learned in a supervised or unsupervised manner. For a supervised version of the deep SOM, we propose to carry out inference with a linear SVM. A forward-backward procedure helps to converge to an optimal feature set. We show by experiments on real large-scale biomedical data set that the proposed methods embed data in a new compact meaningful representation, allow to visualise biomedical signatures, and also lead to a reasonable classification accuracy compared to the state-of-the-art methods.
%B International Joint Conference on Neural Networks (IJCNN)
%8 2016/07/24-29
%$ 122 ; 050 ; 020 ; 084