Dictionary learning on multiple manifolds for image classification

Traditional image classification algorithms based on sparse coding do not consider the relationships among different features. A dictionary learning algorithm was developed to overcome this shortcoming, in which the intrinsic geometrical structure of the multiple manifolds explicitly models the features embedded into a sparse coding algorithm. A coordinate descent algorithm was then used to solve the optimization problem. A convergence analysis was given that shows the algorithm will converge. Tests on three standard benchmark datasets show that the algorithm outperforms other state-of-the-art image classification algorithms.