LDA based compact and discriminative dictionary learning for sparse coding

The dictionary response usually affects the recognition results directly as it represents the original data and usually serves as the input of the classifier. However, the over-complete dictionary usually results in high dimensional response and redundancy. The application of the linear discriminant analysis (LDA)-based mapping method transforms the original dictionary response to be more discriminative for compact dictionary learning, resulting in high intra-class similarity and high inter-class dissimilarity in the response domain for better classification. By analyzing the recognition rate, the compactness and the purity, the proposed method can learn a small size of compact and discriminative dictionary with global optimization, and it can get a comparable or even better performance than the over-complete dictionary with much less computation cost. Experimental results demonstrate that the proposed approach also outperforms several recently proposed compact dictionary learning methods on human action recognition and object classification.