Non-negative Matrix Factorization Parallel Optimization Algorithm Based on Lp-norm
Non-negative matrix factorization algorithm is an important tool for image clustering,data compression and feature ex-traction.Traditional non-negative matrix factorization algorithms mostly use Euclidean distance to measure reconstruction error,which has shown its effectiveness in many tasks,but still has the problems of suboptimal clustering results and slow conver-gence.To solve these problems,the loss function of non-negative matrix factorization is reconstructed by Lp-norm to obtain better clustering results by adjusting the coefficient p.Based on the collaborative optimization theory and Majorization-Minimization al-gorithm,this paper uses the particle swarm optimization to solve the non-negative matrix factorization problem of reconstruction in parallel.The feasibility and effectiveness of the proposed method is verified in real datasets,and the experimental results show that the proposed algorithm significantly improves program execution efficiency and outperforms the traditional non-negative ma-trix decomposition algorithm in a series of evaluation metrics.
Non-negative matrix factorizationLp-normClusteringParallel optimizationRate of convergence
万方数据
维普
