  Recently, covariance descriptors have received much attention as powerful
representations of set of points. In this research, we present a new metric
learning algorithm for covariance descriptors based on the Dykstra algorithm,
in which the current solution is projected onto a half-space at each iteration,
and runs at O(n^3) time. We empirically demonstrate that randomizing the order
of half-spaces in our Dykstra-based algorithm significantly accelerates the
convergence to the optimal solution. Furthermore, we show that our approach
yields promising experimental results on pattern recognition tasks.
