Multi-View Clustering with Spectral Structure Fusion

Multi-view clustering is an important and challenged task due to the difficulty of integrating information from diverse views. After years of research it still faces two challenged questions to date. First, how to integrate heterogeneous information from different views effectively and reduce information loss. Second, how to perform graph learning and spectral clustering simultaneously, avoiding suboptimal clustering results caused by two-step strategy. Integrating heterogeneous information in the data space may cause significant information loss because of unavoidable noise hidden in the data itself or inconsistency among views. Moreover, we consider the case that different views admit the same cluster structure. To fill these gaps, a novel multi-view clustering model with spectral structure fusion is proposed, which fuses the information in the stage of spectral embedding. On the one hand, it avoids the influence of noise and difference of data from diverse views; on the other hand, the fusion position and method are more natural, which reduces the loss of information in the fusion stage. Besides, the model utilizes subspace self-representation for graph learning and integrates graph learning and spectral clustering into a unified framework effectively by joint optimization learning. Experiments on five widely used data sets confirm the superiority and validity of the proposed method.