High-dimensional canonical correlation analysis

Canonical correlations have two faces: from one side, in statistics they give a way to measure dependence between two datasets. From the other side, in linear algebra they represent a canonical form to which a pair of two linear subspaces can be transformed. In this talk we will discuss the theory of canonical correlations when the dimensions of the subspaces are large, in the setting related to both random matrix theory and high-dimensional statistics.