A metamorphosis of Canonical Correlation Analysis into Multivariate Maximum Margin Learning
Canonical Correlation Analysis(CCA) is a useful tool to discover relationship between different sources of information represented by vectors. The solution of the underlying optimisation problem involves a generalised eigenproblem and is nonconvex. We will show a sequence of transformations which turn CCA into a convex maximum margin problem. The new formulation can be applied for the same class of problems at a significantly lower computational cost and with a better numerical stability.