  Recent years have witnessed the success of dictionary learning (DL) based
approaches in the domain of pattern classification. In this paper, we present
an efficient structured dictionary learning (ESDL) method which takes both the
diversity and label information of training samples into account. Specifically,
ESDL introduces alternative training samples into the process of dictionary
learning. 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 computationally expensive L1-norm constraint on the
coefficient matrix, ESDL employs 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.
