Flexible Multi-view Clustering with Dynamic Views Generation

Multi-view clustering is one of the fundamental unsupervised multimedia analysis tasks. Recent studies have mainly focused on developing multi-view clustering approaches, which can achieve state-of-the-art clustering performance. However, most of the existing works just focus on multi-view clustering with fixed views, which lacks flexibility with guidance of the views dynamically generated. Besides, these works ignore to integrate generating views in a dynamic manner and learning the common representation shared by different views into a unified framework. To this end, we propose the Flexible Multi-view Clustering with Dynamic Views Generation (FMCDVG). Specifically, FMCDVG adopts the graph convolutional network and auto-encoder to dynamically generate the topological graph representation and node attribute representation as two different views, respectively. FMCDVG introduces the latent representation shared by different feature representations and integrates multiple feature representations based on node attributes and graph structure into the latent representation with reconstruction through reconstructed encoding networks (REN). FMCDVG jointly conducts generating views in a dynamic manner and learning the common representation shared by different views in a unified optimization framework. We demonstrate that FMCDVG is able to consistently achieve better clustering performance than the state-of-the-art methods through comprehensive experiments.