  To better select the correct training sample and obtain the robust
representation of the query sample, this paper proposes a discriminant-based
sparse optimization learning model. This learning model integrates discriminant
and sparsity together. Based on this model, we then propose a classifier called
locality-based discriminant sparse representation (LDSR). Because discriminant
can help to increase the difference of samples in different classes and to
decrease the difference of samples within the same class, LDSR can obtain
better sparse coefficients and constitute a better sparse representation for
classification. In order to take advantages of kernel techniques, discriminant
and sparsity, we further propose a nonlinear classifier called kernel
locality-based discriminant sparse representation (KLDSR). Experiments on
several well-known databases prove that the performance of LDSR and KLDSR is
better than that of several state-of-the-art methods including deep learning
based methods.
