Sub-coding and Entire-coding Jointly Penalty Based Sparse Representation Dictionary Learning

Currently,the penalty function of dictionary learning(DL)used for sparse representation classification has many versions and each of them has its own advantages.This paper presented a new dictionary learning method called Sub-coding and Entire-coding jointly penalty based dictionary learning,which jointly adds sub-coding penalty functions and entire-coding penalty functions into the dictionary learning objective function.Sub-coding penalty function makes the dictionary after learning can use its reconstruction error and sub-coding for classification,and entire-coding penalty function makes the dictionary after learning can directly use its whole coding for classification at the same time.By combining these two penalty function,good recognition effect can be got.The proposed method is extensively evaluated on emotion speech database and face database in comparison with famous DL based sparse representation classification methods DKSVD and FDDL,and other famous recognition method SRC and SVM.The experimental results show that the proposed method has better recognition performance.