Canonical Correlation Analysis
Canonical Correlation Analysis computes a linear projection which maximizes the correlation between two random variables andn enforces orthogonality in the new space. This is commonly used in multi-modal representation learning, where the random variables are modalities.
Extensions of CCA include Kernel CCA, which uses a non-linear projection, and Deep CCA, which addresses the scalability issues of KCCA.