%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Sokolovska, N.
%A Rizkalla, S.
%A Clément, K.
%A Zucker, Jean-Daniel
%T Continuous and discrete deep classifiers for data integration
%B Advances in intelligent data analysis XIV
%C Cham
%D 2015
%E Fromont, E.
%E De Bie, T.
%E Van Leeuwen, M.
%L fdi:010072191
%G ENG
%I Springer
%@ 978-3-319-24465-5
%M ISI:000389228500023
%N 9385
%P 264-274
%R 10.1007/978-3-319-24465-5_23
%U https://www.documentation.ird.fr/hor/fdi:010072191
%> https://www.documentation.ird.fr/intranet/publi/depot/2018-02-09/010072191.pdf
%W Horizon (IRD)
%X 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.
%S Lecture Notes in Computer Science
%B IDA : Intelligent Data Analysis 2015
%8 2015/10/22-24
%$ 122 ; 050 ; 054