Deep Incomplete Multiview Clustering via Information Bottleneck for Pattern Mining of Data in Extreme-Environment IoT

Internet of Things (IoT) in extreme environments inevitably produces incomplete multiview data, presenting challenges to the existing data analysis methods. Although incomplete multiview clustering (IMC) methods have the potential to mine patterns of incomplete IoT data, they are still confronted with two challenges: 1) they ignore shifts of semantics caused by missing data in aggregating consistent and complementary information of incomplete data, degrading the robustness of models in pattern mining and 2) most of them rely on the instances with complete views as pairwise supervision to capture correlations among views, failing to mine inherent patterns of data in the extreme view missing scenario where multiview instances are only with an available view. To this end, a deep IMC (DIMC) network is proposed via defining dual consistencies within the information bottleneck (IB) framework to mine accurate patterns of incomplete data. Specifically, an unsupervised multiview IB (MIB) is formulated to model dependencies of data, which remedies shifts of semantics via within-view intrinsic knowledge learning, consistent semantics sharing, and consistent structure aligning. Meanwhile, dual consistencies are designed to implement MIB, which builds invariant transformations to mine correlations between views without the help of complete instances. Finally, extensive experiments on four benchmark incomplete data sets demonstrate the superiority of DIMC. Especially, DIMC surpasses the state-of-the-art methods by 0.2048 in accuracy under extreme view missing scenarios.