## 1 Introduction  
Dictionary learning and sparse representation are considerably used in the different approaches of research areas, particularly in image restoration [1, 2, 3, 4], compressive sensing in ad hoc wireless sensors networks and image processing areas [3, 5, 6], face and gender classification [7, 8, 9, 10, 11, 12, 13, 14], action recognition [15, 16, 17, 18], face recognition and object detection [19, 20, 21], etc. A dictionary consists of a collection of column vectors trained in such a way that examples of a dataset belonging to a particular domain can be expressed as linear combinations of a few column vectors of the collection. The column vectors are called atoms. For example, a sample taken from a particular class of dataset can be expressed as a sparse linear combination of the remaining samples of the same class. The atoms contributing to the linear combination to reconstruct a signal are few. Therefore, the weights associated with these atoms form a sparse representation of the signal. These weights are also called sparse weights. The sparse representation power of a dictionary was first exploited by [PERSON] et al. [9], proposing a sparse representation-based classification scheme (SRC). The test signal was sparsely represented over an overcomplete dictionary that was constructed by the labeled training signals. The test signal was constructed with the linear combination of dictionary atoms and the maximum count of class-specific atoms contributing to the linear combination decides the class of the test sample. Although the classification accuracy of SRC was good, computationally it was very expansive. Instead of using training data as a dictionary, discriminative dictionary-learning techniques were introduced for classification. These techniques learned discriminative dictionaries of reasonable sizes and resulted in improved accuracy and efficiency [12; 22]. Instead of directly using labeled training examples to learn a linear classifier, sparse representations of these training examples were also used to learn linear classifiers separately. Joint classifier learning along with discriminative dictionary learning techniques became very effective and efficient [23; 24; 25]. The association of labels with the dictionaries and the classifiers makes the dictionary and the classifier discriminative. Discriminative learning enhances classification accuracy [19; 24; 25]. For a discriminative dictionary to be used for classification purposes, a dictionary may be divided into three categories. In the first category, it consists of two types of atoms, i.e., the class-specific atoms contributing to the representation of data samples belonging to a particular class and the atoms contributing to the representation of all samples of the data [8; 26; 27]. The second category consists of atoms grouped among classes, where each class of atoms represents the data belonging to that class only [3; 14; 28; 29; 30]. In the third category of dictionaries, a data sample is represented as a sparse linear combination of atoms selected dictionary-wide without grouping the atoms among classes. However, discrimination in the dictionary is induced during learning in such a way that sparse weights become discriminative and are used for classification [8; 31]. These three types of discriminative features are equally applicable to a dictionary classifier, which either consists of a single dictionary or a classifier that has been learned in conjunction with a dictionary. The authors' key objective remains to keep the relationship between data samples and the corresponding labels intact while inducing discriminative behavior. In conjunction with discriminative learning, [PERSON] et al. [24; 25] introduced approaches for joint learning of dictionary and a linear classifier based upon a nonparametric Bayesian framework coupled with beta-Bernoulli processes.