Spectral Clustering Algorithm Based on Weighted Ensemble Nyström Sampling

Since most Nyström methods have problems of unstable clustering effect and weak representativeness in spectral clustering application,a spectral clustering algorithm based on weighted ensemble Nyström sampling is proposed. Firstly, the statistical leverage score is used to distinguish the importance of data and the data are weighted. Then, based on these weights, the weighted K-means center point sampling is used to obtain multiple sets of sampling points. The integration framework is introduced, and the approximate kernel matrix is constructed using the cluster parallel operation Nyström method. Finally, the approximate kernel is determined by the ridge regression method. The matrices are combined to produce a more accurate low rank approximation than that by standard Nyström method. Experiments on UCI datasets demonstrate that the proposed algorithm achieves better clustering results. © 2019, Science Press. All right reserved.