An efficient augmented Lagrangian algorithm for graph regularized sparse coding in clustering

The combination of sparse coding and manifold learning has received much attention recently. However, the computational complexity of the resulting optimization problem hinders its practical application. In this paper, an augmented Lagrangian method is proposed to address this issue, which first transforms the unconstrained problem to an equivalent constrained problem and then an alternating direction method is used to iteratively solve the subproblems. Experimental results validate the effectiveness of the propose algorithm.