Dictionary learning enhancement framework: Learning a non-linear mapping model to enhance discriminative dictionary learning methods

Abstract

In this paper, a new framework is presented to enhance the reconstruction and discrimination capabilities of existing discriminative dictionary learning methods. In the proposed framework, a non-linear mapping model is introduced to learn a feature space in a way that any standard discriminative dictionary learning algorithms could achieve higher classification accuracies. The proposed feature mapping process targets to boost the standard dictionary learning methods by facilitating their optimization process. The mapping model uses a modified autoencoder network to provide a higher level of reconstruction and discrimination capabilities for the discriminative dictionary learning methods. The proposed dictionary learning enhancement (DLE) framework could be applied to any discriminative dictionary learning methods with the embedded discriminative term in their objective functions. Our experiments on several real-world image datasets demonstrate that the proposed framework could improve the classification accuracies of standard discriminative dictionary learning methods.