CCAGFA: Bayesian canonical correlation analysis and group factor analysis

R package CCAGFA implements variational Bayesian solution for canonical correlation analysis, inter-battery factor analysis and group factor analysis. The package contains code for learning the model and some supporting functionality for interpretation.

The Bayesian CCA model as implemented here was originally presented by Virtanen et al. (2011), but a more comprehensive treatment is found in Klami et al. (2013). The latter also explains the BIBFA model. The GFA extends CCA to multiple data sources (or groups of variables), providing interpretable linear factorizations that describe variation shared by all possible subsets of sources. It was presented by Virtanen et al. (2012).

The code is available in CRAN at page http://cran.r-project.org/web/packages/CCAGFA/.

Publications

• Arto Klami, Seppo Virtanen, Eemeli Leppäaho, and Samuel Kaski. Group factor analysis. IEEE Transactions on Neural Networks and Learning Systems, 26(9):2136-2147, 2015. [Link] [PDF] [Preprint] [More info]
• Arto Klami, Seppo Virtanen, and Samuel Kaski. Bayesian canonical correlation analysis. Journal of Machine Learning Research, 14:965–1003, 2013. PDF (502 kB) [More info]
• Seppo Virtanen, Arto Klami, Suleiman A. Khan, and Samuel Kaski. Bayesian group factor analysis. In Neil Lawrence and Mark Girolami, editors, Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, volume 22 of JMLR W&CP, pages 1269–1277. JMLR, 2012. PDF (444 kB) [More info] [See also: jmlr.csail.mit.edu ...]
• Seppo Virtanen, Arto Klami, and Samuel Kaski. Bayesian CCA via group sparsity. In Lise Getoor and Tobias Scheffer, editors, Proceedings of the 28th International Conference on Machine Learning (ICML-11), ICML '11, pages 457–464, New York, NY, 2011. ACM. PDF (258 kB) [More info]