Locality preserving discriminative dictionary learning

In this paper, a novel discriminative dictionary learning approach is proposed that attempts to preserve the local structure of the data while encouraging discriminability. The reconstruction error and sparsity inducing ℓ₁-penalty of dictionary learning are minimized alongside a locality preserving and discriminative term. In this setting, each data point is represented by a sparse linear combination of dictionary atoms with the goal that its k-nearest same-label neighbors are preserved. Since the class of a new data point is unknown, its sparse representation is found once for each class. The class that produces the lowest error is associated with that point. Experimental results on five common classification datasets, show that this method outperforms state-of-the-art classifiers, especially when the training data is limited.