Correlation preserved dictionary learning for sparse representation

Sparse representation(SR) based classification has recently led to promising results in image classification, while the performance of classification relies on the quality of SR. However, most of the existing SR approaches failed to consider the geometrical structure of dictionary, which has been shown essential for classification. In this paper, we propose a reinforced SR algorithm by jointly preserving data structure consistency for sparse coding and dictionary correlation for dictionary learning. Specifically, we utilize an inconsistency regularization term to enforce structure consistency between data and SR. In addition, a new non-correlation regularization term is introduced to preserve the correlations between dictionary atoms. Therefore, the learned sparse representations will simultaneously respect the data structure and dictionary correlation. Some experiments carried out with two standard image databases validate the effectiveness of the proposed method for image classification.