D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data. (arXiv:2001.02856v3 [stat.ML] UPDATED)

Modern biomedical studies often collect multi-view data, that is, multiple
types of data measured on the same set of objects. A popular model in
high-dimensional multi-view data analysis is to decompose each view's data
matrix into a low-rank common-source matrix generated by latent factors common
across all data views, a low-rank distinctive-source matrix corresponding to
each view, and an additive noise matrix. We propose a novel decomposition
method for this model, called decomposition-based generalized canonical
correlation analysis (D-GCCA). The D-GCCA …

analysis arxiv canonical correlation data