Focused on the problem that when traditional Non-negative Matrix Factorization (NMF) is applied to clustering, robustness and sparsity are not considered at the same time, which leads to low clustering performance, a sparse Non-negative Matrix Factorization algorithm based on Kernel technique and HyperGraph regularization (KHGNMF) was proposed. Firstly, on the basis of inheriting good performance of kernel technique, L _2,1 norm was used to improve F-norm of standard NMF, and hyper-graph regularization terms were added to preserve inherent geometric structure information among the original data as much as possible. Secondly, L _2,1/2 pseudo norm and L _1/2 regularization terms were merged into NMF model as sparse constraints. Finally, a new algorithm was proposed and applied to image clustering. The experimental results on six standard datasets show that KHGNMF can improve clustering performance (accuracy and normalized mutual information) by 39% to 54% compared with nonlinear orthogonal graph regularized non-negative matrix factorization, and the sparsity and robustness of the proposed algorithm are increased and the clustering effect is improved.

Concerning the low accuracy of existing software birthmark extraction algorithms in detecting code theft problem, a new static software birthmark extraction algorithm was proposed. The birthmark generated by this algorithm covered two kinds of software features. The source program and the suspicious program were preprocessed to get the program meta data, which was used to generate Application Programming Interface (API) call set and instruction sequence as two features. These two features were synthesized to generate software birthmarks. Finally, the similarity of source program and suspicious program was calculated to determine whether there was code theft between the two programs. The experimental result verifies that the birthmark combined by API call set and instruction sequence has credibility and resilience, and has stronger resilience compared with k-gram birthmark.