  We propose a novel approach to the analysis of covariance operators making
use of concentration inequalities. First, non-asymptotic confidence sets are
constructed for such operators. Then, subsequent applications including a k
sample test for equality of covariance, a functional data classifier, and an
expectation-maximization style clustering algorithm are derived and tested on
both simulated and phoneme data.
