text: This paper proposes a method of learning sparse dictionary learning by introducing new types of priors.  Specifically, they designed a novel idea of defining a metric to measure discriminative properties along with the quality of presentations. It is also presented the power of the proposed method in comparison with the existing methods in the literature. Overall, the paper deals with an important issue in dictionary learning and proposes a novel idea of utilizing a set of priors.  To this reviewer’s understanding, the thresholding parameter $\tau_{c}$ is specific for a class $c$ only,  thus different classes have different $\tau$ vectors.  If so, Eq. (6) for approximation of the measure $D(\cdot)$ is not clear how the similarity measure between ${\bf y}_{c,k}$ and ${\bf y}_{c1,k1}$, \ie, $\left\|{\bf y}_{c,k}^{+}\odot{\bf y}_{c1,k1}^{+}\right\|_{1}+\left\|{\bf y}_{c,k}^{+}\odot{\bf y}_{c1,k1}^{+}\right\|_{1}$ and $\left\|{\bf y}_{c,k}\odot{\bf y}_{c1,k1}\right\|_{2}^{2}$, works to approximate it.  It would be appreciated to give more detailed description on it and geometric illustration, if possible. There are many typos and grammatical errors,  which distract from reading and understanding the manuscript.