Variable Selection in the Regularized Simultaneous Component Analysis Method for Multi-Source Data Integration.

ABSTRACT: Interdisciplinary research often involves analyzing data obtained from different data sources with respect to the same subjects, objects, or experimental units. For example, global positioning systems (GPS) data have been coupled with travel diary data, resulting in a better understanding of traveling behavior. The GPS data and the travel diary data are very different in nature, and, to analyze the two types of data jointly, one often uses data integration techniques, such as the regularized simultaneous component analysis (regularized SCA) method. Regularized SCA is an extension of the (sparse) principle component analysis model to the cases where at least two data blocks are jointly analyzed, which - in order to reveal the joint and unique sources of variation - heavily relies on proper

SUBMITTER: Gu Z 

PROVIDER: S-EPMC6901488 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature