Transductive Multi-Label Learning via Alpha Matting

Multi-label learning deals with the problems when each instance can be assigned to multiple classes simultaneously, which are ubiquitous in real-world learning tasks. In this paper, we propose a new multilabel learning method, which is able to exploit unlabeled data to obtain an effective model for assigning appropriate multiple labels to instances. The proposed method is called T (TRansductive multi-label learning via Alpha Matting), which formulates transductive multi-label learning as an optimization problem. We develop an efficient algorithm which has a closed form solution to solve this optimization problem. Empirical studies on real-world multi-label learning tasks show that T can effectively make use of unlabeled data information to achieve performance as good as existing state-of-the-art multi-label learning algorithms, moreover T is much faster and can handle relatively larger data sets.