Deep kernel dimensionality reduction for scalable data integration

Abstract

International audienceDimensionality 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

In Search of Big Medical Data Integration Solutions - A Comprehensive Survey

“…As well, the volume challenge can be reduced using dimensionality reduction. Methods such as principal component analysis (PCA), Multimodal deep learning, Isomap have proven their effectiveness in dimensionality reduction [126].…”

Fast and Efficient Conflict Identification and Resolution in Huge Streaming Data

“…They propose flexible structures to efficiently store, retrieve and process data. A deep kernel dimensionality reduction technique was proposed in [21]. This technique has a scalability advantage, hence has the ability to adapt depending on the data size.…”