Discriminative canonical correlation analysis (DCCA) is a powerful supervised feature extraction technique for two sets of multivariate data, which has wide applications in pattern recognition. DCCA consists parts: (i) mean-centering that subtracts the sample mean from and (ii) solving generalized eigenvalue problem. The cost expensive when dealing with large number high-dimensional samples. To...

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 ...

This paper introduces a new non-linear feature extraction technique based on Canonical Correlation Analysis (CCA) with applications in regression and object recognition. The non-linear transformation of the input data is performed using kernel-methods. Although, in this respect, our approach is similar to other generalized linear methods like kernel-PCA, our method is especially well suited for...