Consensus-Anchored Residual Learning for Incomplete Multi-View Multi-Label Classification

Incomplete multi-view multi-label classification seeks to leverage heterogeneous information from partially observed views and labels. Existing approaches often couple consensus learning with the extraction of view-specific information, allowing complementary updates to perturb the shared predictor. Under sparse supervision, such coupling may compromise reliable consensus and diminish the predictive gains from view-specific information. To address this issue, we propose Consensus-Anchored Residual Learning (CARE), a new two-stage framework that preserves shared knowledge while learning complementary corrections. Specifically, the first stage establishes a consensus predictor through cross-view fusion guided by label semantics. The second stage anchors this predictor and learns residual refinements around it, while retaining the original consensus as an incumbent during model selection. This design provides a stable reference for exploiting view-specific information and evaluating its predictive contribution. Furthermore, our analysis characterizes conditions for beneficial residual updates and relates the gains retained after selection to conditional complementary information, approximation error, and selection uncertainty. Extensive experiments on six benchmark datasets demonstrate the superiority of CARE over state-of-the-art methods, together with its effectiveness in chest radiograph screening.