Multi-View Clustering With Incremental Instances and Views

Multi-view clustering (MVC) has attracted increasing attention with the emergence of various data collected from multiple sources. In real-world dynamic environment, instances are continually gathered, and the number of views expands as new data sources become available. Learning for such simultaneous increment of instances and views, particularly in unsupervised scenarios, is crucial yet underexplored. In this paper, we address this problem by proposing a novel MVC method with Incremental Instances and Views, MVC-IIV for short. MVC-IIV contains two stages, an initial stage and an incremental stage. In the initial stage, a basic latent multi-view subspace clustering model is constructed to handle existing data, which can be viewed as traditional static MVC. In the incremental stage, the previously trained model is reused to guide learning for newly arriving instances with new views, transferring historical knowledge while avoiding redundant computations. In specific, we design and reuse two modules, i.e., multi-view embedding module for low-dimensional representation learning, and consensus centroids module for cluster probability learning. By adding consistency regularization on the two modules, the knowledge acquired from previous data is used, which not only enhances the exploration within current data batch, but also extracts the between-batch data correlations. The proposed model can be efficiently solved with linear space and time complexity. Extensive experiments demonstrate the effectiveness and efficiency of our method compared with the state-of-the-art approaches.