Joint Spectrum Embedding Clustering Algorithm Based on Multi-view Diversity Learning

Most of the existing multi-view clustering algorithms only rely on the low-order similarity information between views,fail to capture the high-order structural features in the data effectively,and pay insufficient attention to the diversity features of the multi-view data,resulting in the accuracy and robustness of the clustering results.To solve these problems,JSEC,a joint spectral embedding clustering algorithm based on multi-view diversity learning,is proposed.Through view diversity learning,multiple features between data are preserved,so as to effectively remove the noise in the view.Then,a method of mining higher-order information of views is proposed to make the diversity features of views as close as possible to the hybrid similarity graph,so as to realize efficient integration of information of different views,and realize the diversity and complementary integration of views.Finally,the diversity feature matrix of the view is fused into the joint spectral embedding matrix in the spectral embedding module,and the graph clustering is realized by spectral clustering.In addition,an alternate iteration method is designed to optimize the objective function.In comparison with the latest multi-view clustering algorithms,JSEC algorithm shows superior performance on 3 indicators of 5 medium and small scale real datasets,and also on 2 large scale datasets.Compared with the suboptimal algorithm,ARI index has an improvement of 1.27% and 2.57% ondatasets of different scales.The superiority of the algorithm is proved theoretically and experimentally.