Diversity Multi-View Clustering With Subspace and NMF-Based Manifold Learning

Since the complementarity information among multiple views has been exploited to improve the clustering effect significantly, multi-view clustering has become a hot topic, and many multi-view clustering methods have emerged. Most of them only consider local features in each view, ignoring the differences in the manifold structure of the same class samples among different views. In addition, they need to balance the importance of respective views effectively, thus ignoring the diversity among views in the clustering process. To address these problems, we propose a new diversity multi-view clustering method with subspace and NMF-based manifold learning. Firstly, non-negative matrix factorization and manifold learning are used to obtain features and local geometric structures of samples. After that, the latent space representation facilitates the transfer of manifold structural features between views and improves the class consistency of the same sample in different views. Moreover, the Hilbert-Schmidt independence criterion is introduced to learn diversity for mutual learning and information fusion among views. Finally, experiments on seven datasets demonstrate the superiority of the proposed method compared to ten state-of-the-art methods.