Weighted locality collaborative representation based on sparse subspace.

Highlights • A novel algorithm based on the sparse subspace is proposed. • The approach first learns a subset of the original training data to build a much correlated dictionary, and learns the reconstruction coefficients for each unlabeled data while considering the influence of its local neighbors. • The approach takes advantages of the linear regression techniques together with the weighted collaborative representation techniques to learn more discriminative representation coefficients for unlabeled data. • Experimental results demonstrate its effectiveness. Abstract This paper takes into account both unlabeled data and their local neighbors to learn their sparse representations, and proposes a face recognition approach named Weighted Locality Collaborative Representation Classifier based on sparse subspace (WLCRC). WLCRC firstly learns a subset of the original training data to build a much correlated dictionary, and then combines linear regression techniques together with weighted collaborative representation techniques to optimize the linear reconstruction of unlabeled data. It uses the newly built dictionary to learn the reconstruction coefficients for each unlabeled datum while considering the influence of its local neighbors. Classifications are performed according to the reconstruction residuals, and experimental results on benchmark datasets demonstrate that WLCRC is effective. [ABSTRACT FROM AUTHOR]