In this paper we present a new approach of incorporating kernels into\ndictionary learning. The kernel K-SVD algorithm (KKSVD), which has been\nintroduced recently, shows an improvement in classification performance, with\nrelation to its linear counterpart K-SVD. However, this algorithm requires the\nstorage and handling of a very large kernel matrix, which leads to high\ncomputational cost, while also limiting its use to setups with small number of\ntraining examples. We address these problems by combining two i…