Reliable Dynamical Systems for Canonical Variate Computation

Canonical correlation analysis is a statistical tool for investigating the presence of any patterns that simultaneously exist in two different data sets and compute the correlation between associated patterns. In this paper, dynamical systems for computing the canonical correlations and canonical variates between two data sets are derived. These systems are developed by optimizing constrained and unconstrained merit functions. To extract the actual individual canonical variates, some of these systems are weighted by a positive definite diagonal matrix, while others employed upper triangular matrices.