Sokolovska N. auteur aut IRD Clément K. auteur aut IRD Zucker Jean-Daniel auteur aut IRD text journalArticle eng text/pdf born digital access Dimensionality reduction is used to preserve significant properties of data in a low dimensional space. In particular, data representation in a lower dimension is needed in applications, where information comes from multiple high dimensional sources. Data integration, however, is a challenge in itself. In this contribution, we consider a general framework to perform dimensionality reduction taking into account that data are heterogeneous. We propose a novel approach, called Deep Kernel Dimensionality Reduction which is designed for learning layers of new compact data representations simultaneously. The method can be also used to learn shared representations between modalities. 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. specialized Dimensionality reduction Heterogeneous data integration 122 050 054 020 084 74 121-132 2016 0888-613X https://www.documentation.ird.fr/hor/fdi:010066930 10.1016/j.ijar.2016.03.008 0888-613X [F B010066930] https://www.documentation.ird.fr/hor/fdi:010066930 https://www.documentation.ird.fr/intranet/publi/2016/06/010066930.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2016-06-29 2017-08-23 fdi:010066930 fre