Missing Values in RGCCA: Algorithms and Comparisons

Regularized generalized canonical correlation analysis (RGCCA) is a general statistical framework for multiblock data analysis. However, multiblock data often have missing structure, i.e., data in one or more blocks may be completely unobserved for a sample. In this work, several solutions were investigated to properly handle missing data structures within the framework of RGCCA then compared on simulations.