Stochastic Dykstra Algorithms for Metric Learning on Positive   Semi-Definite Cone
  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. 
Tomoki Matsuzawa, Raissa Relator, Jun Sese, Tsuyoshi Kato
cs.CV