Shared and individual representation learning with Feature Diversity for Deep MultiView Clustering

Due to the remarkable representation ability of Nonnegative matrix factorization (NMF), its multiview variants have become a crucial kind of multiview representation learning methods. However, the majority of existing methods fail to effectively utilize complex higher-level hierarchical information, shared information, and individual information of multiview data. To address these challenges, we propose the Shared and individual representation learning with feature Diversity for Deep Multiview Clustering (SiDDMVC). Specifically, this method decomposes data of each view through nonnegative matrix factorization in a layer-wise fashion to exploit complex higher-level hierarchical information. The outputs of NMF are then divided into shared and individual representations with shared geometrical constraint and feature diversity constraint. Experimental results on six real-world datasets have shown that SiDDMVC not only achieves superior performance through the use of graph regularization and two diversity constraints, but also surpasses several state-of-the-art multiview clustering methods.