Geodesic Convexity and Regularized Scatter Estimators
  As observed by Auderset et al. (2005) and Wiesel (2012), viewing covariance matrices as elements of a Riemannian manifold and using the concept of geodesic convexity provide useful tools for studying M-estimators of multivariate scatter. In this paper, we begin with a mathematically rigorous self-contained overview of Riemannian geometry on the space of symmetric positive definite matrices and of the notion of geodesic convexity. The overview contains both a review as well as new results. In particular, we introduce and utilize first and second order Taylor expansions with respect to geodesic parametrizations. This enables us to give sufficient conditions for a function to be geodesically convex. In addition, we introduce the concept of geodesic coercivity, which is important in establishing the existence of a minimum to a geodesic convex function. We also develop a general partial Newton algorithm for minimizing smooth and strictly geodesically convex functions. We then use these results to generate a fairly complete picture of the existence, uniqueness and computation of regularized M-estimators of scatter defined using additive geodescially convex penalty terms. Various such penalties are demonstrated which shrink an estimator towards the identity matrix or multiples of the identity matrix. Finally, we propose a cross-validation method for choosing the scaling parameter for the penalty function, and illustrate our results using a numerical example. 
Lutz Duembgen and David E. Tyler
stat.ME