Anti-Degradation Lifelong Multi-View Clustering

In real-world scenarios, new views are continuously collected over time, forming a dynamic view stream. To handle such evolving data, a lifelong multi-view clustering framework is needed instead of a static model. However, large discrepancies across views make it challenging to learn new knowledge while preserving previously acquired information. There are few methods use consistency alignment or knowledge distillation to align new knowledge with old ones. However, these strategies cannot fundamentally prevent knowledge degradation, since new knowledge inevitably interferes with the learned representation space. To overcome this limitation, we propose a new \textbf{A}nti-degradation Lifelong Multi-view Clustering (ALMC) framework. Specifically, we innovatively propose a null-space-projection knowledge base anti-degradation technique, which ensures that new knowledge updates to the model only occur in directions orthogonal to the retained knowledge, thus preventing catastrophic forgetting of knowledge and degradation of clustering performance, and provides theoretical proof for this. Extensive experiments on multiple multi-view benchmark datasets demonstrate superior performance in multi-view clustering.