Horizon 1 1 17 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES Advances in intelligent data analysis XIV Lecture Notes in Computer Science IDA : Intelligent Data Analysis 2015 264-274 2015 2015/10/22-24 Cham Springer fdi:010072191 ENG ISI:000389228500023 9385 10.1007/978-3-319-24465-5_23 https://www.documentation.ird.fr/hor/fdi:010072191 https://www.documentation.ird.fr/intranet/publi/depot/2018-02-09/010072191.pdf Horizon (IRD) 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. 122 ; 050 ; 054 UR209