Discriminative label consistent dictionary learning

The goal of this work is to improve dictionary learning techniques for classification. We primarily focus on the label consistent K-SVD technique. We improve the consistency between the linear classification and class labels by introducing a sigmoid function. The second improvement is in replacing the Euclidean norm for the consistency constraint by a robust l_p-norm (0<;p<;1); this makes the inconsistency robust to changes in magnitude. We compare our proposed modifications with existing work on label consistent KSVD and Sparse Classifier on the Extended YaleB and the AR face databases. Our proposed formulations show considerable improvement in accuracy compared to the baselines.