High-performance Computing and Application of Zero-norm

Whether sparseness can be effectively controlled is one of the key elements to measure the merits of the sparse coding algorithm. One-norm is primarily used in the sparse coding algorithm to control its sparseness currently, as well as by sparse approximation to control the sparseness of sparse coding model, but all these methods have led to slow convergence and low efficiency ultimately. In order to enhance the effectiveness of sparse coding algorithms, this paper selects zero-norm to control sparseness of the sparse coding model, and calculate after continuously extended at the discontinuous point of the model. We propose a highly efficient zero-norm sparse coding algorithm. This paper not only theoretically proves feasibility and efficiency of the algorithms which is capable of effectively controlling model sparseness, but also verifies the theoretical correctness of inference through experiments. These prove the operational efficiency of the algorithm is more efficient and stronger than existing algorithms.