  Feature alignment methods are used in many scientific disciplines for data
pooling, annotation, and comparison. As an instance of a permutation learning
problem, feature alignment presents significant statistical and computational
challenges. In this work, we propose the covariance alignment model to study
and compare various alignment methods and establish a minimax lower bound for
covariance alignment that has a non-standard dimension scaling because of the
presence of a nuisance parameter. This lower bound is in fact minimax optimal
and is achieved by a natural quasi MLE. However, this estimator involves a
search over all permutations which is computationally infeasible even when the
problem has moderate size. To overcome this limitation, we show that the
celebrated Gromov-Wasserstein algorithm from optimal transport which is more
amenable to fast implementation even on large-scale problems is also minimax
optimal. These results give the first statistical justification for the
deployment of the Gromov-Wasserstein algorithm in practice.
