Canonical Correlation Analysis (CCA) computes maximally-correlated linear projections of two modalities. We propose Differentiable CCA, a formulation of CCA that can be cast as a layer within a multi-view neural network. Unlike Deep CCA, an earlier extension of CCA to nonlinear projections, our formulation enables gradient flow through the computation of the CCA projection matrices, and free ch...

We discuss algorithms for performing canonical correlation analysis. In canonical correlation analysis we try to find correlations between two data sets. The canonical correlation coefficients can be calculated directly from the two data sets or from (reduced) representations such as the covariance matrices. The algorithms for both representations are based on singular value decomposition. The ...