A Graph-based Semi-supervised Multi-label Learning Method Based on Label Correlation Consistency

Multi-label learning deals with the problem which each data example can be represented by an instance and associated with a set of labels, i.e., every example can be classified into multiple classes simultaneously. Most of the existing multi-label learning methods are supervised which cannot deal with such application scenarios where manually labeling the data is very expensive and time-consuming while the unlabeled data are very cheap and easy to obtain. This paper proposes an ensemble learning method which integrates multi-label learning and graph-based semi-supervised learning into one framework. The label correlation consistency is introduced to deal with the multi-label learning. The proposed method has been evaluated on five public multi-label datasets by comparing it with state-of-the-art supervised and semi-supervised multi-label methods according to multiple evaluation metrics to confirm its effectiveness. Experimental results show that the proposed method can achieve the comparable performance compared with the state-of-the-art methods. Furthermore, it is more confident on every single predicted label.