Matlab code for two-stage kernel cca
Canonical correlation analysis (CCA) is a statistical tool for finding linear associations between different types of information. Previous extensions of CCA used to capture nonlinear associations, such as kernel CCA, did not allow feature selection or capturing of multiple canonical components. Here we propose a novel method, two-stage kernel CCA (TSKCCA) to select appropriate kernels in the framework of multiple kernel learning.
reference
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1543-x