Sokolovska N., Rizkalla S., Clément K., Zucker Jean-Daniel. (2015). Continuous and discrete deep classifiers for data integration. In :
Fromont E. (ed.), De Bie T. (ed.), Van Leeuwen M. (ed.). Advances in intelligent data analysis XIV. Cham : Springer, p. 264-274. (Lecture Notes in Computer Science ; 9385). IDA : Intelligent Data Analysis 2015, 14., Saint-Etienne (FRA), 2015/10/22-24. ISBN 978-3-319-24465-5.
Titre du document
Continuous and discrete deep classifiers for data integration
Sokolovska N., Rizkalla S., Clément K., Zucker Jean-Daniel
In
Fromont E. (ed.), De Bie T. (ed.), Van Leeuwen M. (ed.), Advances in intelligent data analysis XIV
Source
Cham : Springer, 2015,
p. 264-274 (Lecture Notes in Computer Science ; 9385). ISBN 978-3-319-24465-5
Colloque
IDA : Intelligent Data Analysis 2015, 14., Saint-Etienne (FRA), 2015/10/22-24
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.
