Sokolovska N., Clément K., Zucker Jean-Daniel. (2016). Deep kernel dimensionality reduction for scalable data integration. International Journal of Approximate Reasoning, 74, p. 121-132. ISSN 0888-613X.
Titre du document
Deep kernel dimensionality reduction for scalable data integration
International Journal of Approximate Reasoning, 2016,
74, p. 121-132 ISSN 0888-613X
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.
