Anchor-based disentanglement framework for incremental multi-view clustering.

Abstract

Incremental Multi-view Clustering (IMvC) has received increasing attention due to its ability to handle dynamically arriving views. However, most existing IMvC methods enforce a unified representation to facilitate cross-view knowledge transfer, which inevitably suppresses view-specific characteristics. To overcome this, we propose a novel Anchor-based Disentanglement Framework for Incremental Multi-view Clustering (ADIMC), which introduces disentanglement representation learning to enable conflict-free knowledge transfer under view-incremental scenarios. Specifically, for each newly arrived view, an anchor graph is first learned to extract the latent semantic information. Subsequently, the anchor graph is decomposed into view-consistent and view-specific components to explicitly disentangle the shared semantics and private information of each view. Based on these, knowledge transfer is restricted to the view-consistent components to maintain cross-view coherence, while the view-specific components are preserved to retain the uniqueness of each view. Combined with an efficient iterative optimization algorithm, ADIMC ensures stable and efficient model updating with limited computing resources. Extensive experiments on multiple benchmark datasets validate the superiority and effectiveness of ADIMC over state-of-the-art methods.