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 dimen-sionality 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 .