A0862
Title: Latent variable methods for multi-view high-dimensional data
Authors: Katrijn Van Deun - Tilburg University (Netherlands) [presenting]
Abstract: Research in many disciplines relies more and more on intensive collections of data representing several points of view. For example, in studying obesity or depression as the outcome of environmental and genetic influences, researchers increasingly collect survey, dietary, biomarker and genetic data from the same individuals. Revealing the variables that are linked throughout these different types of data gives crucial insight into the complex interplay between the multiple factors that determine human behaviour, e.g., the concerted action of genes and environment in the emergence of obesity or depression. Although linked high-dimensional multiview data form an extremely rich resource for research, extracting meaningful and integrated information is challenging and not appropriately addressed by current statistical methods. The challenge is to select those variables that are linked throughout the different blocks, and this eludes currently available methods for data analysis. The first problem is that relevant information is hidden in a bulk of irrelevant variables with a high risk of finding incidental associations. Second, the sources are often very heterogeneous, which may obscure apparent links between the shared mechanisms. The challenges associated with the analysis of large-scale multiview data are discussed, and a sparse common and distinctive latent variable approach is presented to address the challenges.
