Group Factor Analysis
Open Access
- 18 December 2014
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks and Learning Systems
- Vol. 26 (9), 2136-2147
- https://doi.org/10.1109/tnnls.2014.2376974
Abstract
Factor analysis (FA) provides linear factors that describe the relationships between individual variables of a data set. We extend this classical formulation into linear factors that describe the relationships between groups of variables, where each group represents either a set of related variables or a data set. The model also naturally extends canonical correlation analysis to more than two sets, in a way that is more flexible than previous extensions. Our solution is formulated as a variational inference of a latent variable model with structural sparsity, and it consists of two hierarchical levels: 1) the higher level models the relationships between the groups and 2) the lower models the observed variables given the higher level. We show that the resulting solution solves the group factor analysis (GFA) problem accurately, outperforming alternative FA-based solutions as well as more straightforward implementations of GFA. The method is demonstrated on two life science data sets, one on brain activation and the other on systems biology, illustrating its applicability to the analysis of different types of high-dimensional data sources.Keywords
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Funding Information
- Academy of Finland through the Finnish Centre of Excellence in Computational Inference Research COIN (251170, 140057, 266969)
- Tekes-the Finnish Funding Agency for Technology and Innovation through the Data to Intelligence (D2I) ICT SHOK Programme
- Aalto University, Espoo, Finland, through the aivoAALTO Project
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