@incollection{fdi:010072191,
  title = {{C}ontinuous and discrete deep classifiers for data integration},
  author = {{S}okolovska, {N}. and {R}izkalla, {S}. and {C}l{\'e}ment, {K}. and {Z}ucker, {J}ean-{D}aniel},
  editor = {},
  language = {{ENG}},
  abstract = {{D}ata 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. {I}n this contribution, we propose a novel approach, called {D}eep {K}ernel {D}imensionality {R}eduction which is designed for learning layers of new compact data representations simultaneously. {W}e 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. {W}e also consider some state-of-the art deep learners and their corresponding discrete classifiers. {W}e 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.},
  keywords = {},
  booktitle = {{A}dvances in intelligent data analysis {XIV}},
  numero = {9385},
  pages = {264--274},
  address = {{C}ham},
  publisher = {{S}pringer},
  series = {{L}ecture {N}otes in {C}omputer {S}cience},
  year = {2015},
  DOI = {10.1007/978-3-319-24465-5_23},
  ISBN = {978-3-319-24465-5},
  URL = {https://www.documentation.ird.fr/hor/fdi:010072191},
}