Recent years have witnessed the success of dictionary learning (DL)-based approaches in the domain of pattern classification. We present an efficient structured dictionary learning (ESDL) method that takes both the diversity and label information of training samples into account. Specifically, ESDL introduces alternative training samples into the process of DL. To increase the discriminative capability of representation coefficients for classification, an ideal regularization term is incorporated into the objective function of ESDL. Moreover, in contrast with conventional DL approaches, which impose a computationally expensive ℓ₁-norm constraint on the coefficient matrix, ESDL employs an ℓ₂-norm regularization term. Experimental results on benchmark databases (including four face databases and one scene dataset) demonstrate that ESDL outperforms previous DL approaches. More importantly, ESDL can be applied in a wide range of pattern classification tasks.