Multi-label learning (MLL) addresses learning problems in which a single data instance may simultaneously belong to multiple semantic categories. This formulation arises naturally in many real-world applications, including image and video understanding, medical diagnosis, text categorization, and bioinformatics. In many of these settings, it is not sufficient to merely rank relevant labels higher than irrelevant […]