Weighted locality collaborative representation based on sparse subspace.

•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.