Struture-aware Classification Using Supervised Dictionary Learning

In this work, we propose a supervised dictionary learning algorithm, that attempts to preserve the local geometry in both dimensions of the data. A graph-based regularization explicitly takes into account the local manifold structure of the data, and a second graph regularization gives similar treatment to the feature domain and helps in learning a more robust dictionary. Both graphs can be constructed from the training data or learned and adapted along the dictionary learning process. The two terms together promote the discriminative power of the learned sparse representations and lead to improved classification accuracy. The suggested method was evaluated on several different datasets, representing both single-label and multi-label classification problems, and demonstrated better performance compared with other dictionary based approaches.