Sokolovska N. auteur aut IRD Rizkalla S. auteur aut IRD Clément K. auteur aut IRD Zucker Jean-Daniel auteur aut IRD text bookSection eng text/pdf born digital access Data representation in a lower dimension is needed in applications, where information comes from multiple high dimensional sources. A final compact model has to be interpreted by human experts, and interpretation of a classifier whose weights are discrete is much more straightforward. In this contribution, we propose a novel approach, called Deep Kernel Dimensionality Reduction which is designed for learning layers of new compact data representations simultaneously. We show by experiments on standard and on real large-scale biomedical data sets that the proposed method embeds data in a new compact meaningful representation, and leads to a lower classification error compared to the state-of-the-art methods. We also consider some state-of-the art deep learners and their corresponding discrete classifiers. We illustrate by our experiments that although purely discrete models do not always perform better than real-valued classifiers, the trade-off between the model accuracy and the interpretability is quite reasonable. specialized 122 050 054 Fromont E. ed. edt IRD De Bie T. ed. edt IRD Van Leeuwen M. ed. edt IRD 9385 264-274 Cham Springer 2015 IDA : Intelligent Data Analysis 2015, 14., Saint-Etienne (FRA), 2015/10/22-24 9385 https://www.documentation.ird.fr/hor/fdi:010072191 10.1007/978-3-319-24465-5_23 978-3-319-24465-5 [F B010072191] https://www.documentation.ird.fr/hor/fdi:010072191 https://www.documentation.ird.fr/intranet/publi/depot/2018-02-09/010072191.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2018-02-08 2018-05-16 fdi:010072191 fre