Supervised Deep Dictionary Learning for Single Label and Multi-Label Classification

This is the first work that introduces supervision into the deep dictionary learning framework and solves it in an optimal fashion. The derivation for solving the ensuing formulation is based on the state-of-the-art optimization paradigm that includes proximal variable splitting, augmented Lagrangians and alternating direction method of multipliers. Our proposed formulation can handle both single label and multi-label classification problems. Experiments have been carried out on benchmark datasets. Comparison has been carried out with both well known and modern techniques. In every case, our proposed solution surpasses others.