TIGER: Target-Aware Interventional Graph-Structure Decoupling and Retrieval for Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) aims to learn effective clustering representations from multi-view data with partially missing views. Although considerable progress has been made, existing methods still face several challenges: 1) Many approaches recover missing views through direct feature reconstruction or global information sharing, which may ignore the distribution characteristics of the target view and lead to inaccurate representation generation. 2) The structural relationships among samples and the complementary information across views are insufficiently explored during missing-view recovery, making the learned representations sensitive to incomplete and noisy observations. 3) Existing methods usually lack an explicit mechanism to evaluate the reliability of recovered information, causing uncertain completion results to negatively affect representation fusion and clustering optimization. To address these issues, we propose TIGER: Target-aware Interventional Graph-structure Decoupling and Retrieval for Incomplete Multi-view Clustering. Specifically, TIGER first learns view-specific latent representations and separates transferable and view-dependent information to construct more reliable cross-view relationships. Then, a target-aware retrieval strategy is developed to recover missing views by selecting informative anchors from observed target-view samples according to cross-view structural evidence. Furthermore, an intervention-based completion mechanism is introduced to identify and reduce unreliable local correlations, enabling robust representation recovery under severe missing conditions. Finally, the recovered representations are integrated with clustering learning through a unified optimization framework. Extensive experiments on seven benchmark datasets under various missing rates demonstrate that TIGER consistently outperforms state-of-the-art IMVC methods.