Tensor-based Opposing yet Complementary Learning for Multi-view Multi-label Feature Selection

Multi-view multi-label learning (MVML) is a significant area of research in multimedia, providing a foundational framework for various real-world applications. However, the richness of its descriptive capabilities often results in high-dimensional data that contains redundant information, negatively affecting model performance. Most existing methods do not thoroughly explore the mapping of the distinctive parts while balancing common and distinctive information. Additionally, there has been limited focus on the relationships between different types of mappings and the high-order constraints among view-specific labels. In this paper, we propose a novel tensor-based method for view-specific label learning that integrates adaptive weight mechanisms into both global non-linear and local linear mappings. This method effectively captures high-order relationships among views and hybrid labels through hierarchical label correlation constraints. Central to our model is the ''Opposing yet Complementary'' procedure, which enhances feature weight representation at a finer-grained level. Extensive experiments on widely used multi-view multi-label datasets demonstrate significant performance improvements, underscoring the effectiveness of our proposed method.