A Unified Framework to BRIDGE Complete and Incomplete Deep Multi-View Clustering Under Non-IID Missing Patterns

Incomplete multi-view clustering (IMVC) has gained increasing attention due to its ability to analyze incomplete multi-view data. Despite deep IMVC methods achieved significant progress, they still face two challenges: (I) The method-specific inseparable designs limit their application. (II) Non-independent and identically distributed (Non-IID) missing patterns has not been considered and caused degeneration. To address these issues, we propose a novel unified framework that bridges from deep MVC to deep IMVC, while emphasizing the robustness against Non-IID missing patterns. Our framework has a two-stage process: (I) Multi-view learning on complete data, where our framework is modularly established to be compatible with different multi-view interaction objectives. (II) Transfer learning and clustering on incomplete data, where we propose a multi-view domain adversarial learning method to improve the model robustness to Non-IID missing patterns. Moreover, an intra-view and inter-view imputation strategy is introduced for more reliable clustering. Based on our unified framework, we easily construct multiple IMVC instances and extensive experiments verified their clustering effectiveness. The code is available at https://github.com/xiaorui-jiang/BRIDGE.