Concept-Aware View Routing with Sparse Compensation for Multi-View Multi-Label Feature Selection

In recent years, multi-view multi-label learning (MVML) has attracted increasing attention due to its ability to characterize complex real-world objects by exploiting complementary information from multiple views and rich multi-label semantics. However, selecting discriminative features from high-dimensional multi-view data remains a critical challenge in MVML. Most existing methods adopt unified view fusion and shared feature selection strategies, making it difficult to capture the heterogeneous view dependencies associated with different label semantics and potentially overlooking specific features required for challenging semantic concepts. To address these issues, we propose View-Conditioned Route Selection for Multi-View Multi-Label Feature Selection (VCRS). Specifically, VCRS first constructs a semantic space that integrates atomic concepts and relational concepts, thereby jointly preserving label information and higher-order label semantics. Concept confidence, graph constraints, and adaptive view routing are further incorporated to achieve fine-grained concept-level multi-view fusion. In addition, VCRS learns a shared set of base features via nonconvex sparse learning, identifies under-represented concepts according to their concept reconstruction errors and importance, and selects concept-specific features through a sparse compensation mechanism. In this way, VCRS enhances the feature representation of challenging semantic concepts while retaining the advantages of shared feature selection. Experimental results on multiple multi-view datasets demonstrate the effectiveness of the proposed method.