Multi-Label Learning with Label Enhancement

Multi-label learning deals with training instances associated with multiple labels. Many common multi-label algorithms are to treat each label in a crisp manner, being either relevant or irrelevant to an instance, and such label can be called logical label. In contrast, we assume that there is a vector of numerical label behind each multi-label instance, and the numerical label can be treated as the indicator to judge whether the corresponding label is relevant or irrelevant to the instance. The approach we are proposing transforms multi-label problem into regression problem about numerical label. In order to explore the numerical label, one way is to extend the label space to a Euclidean space by mining the hidden label importance from the training examples. Such process of transforming logical labels into numerical labels is called Label Enhancement. Besides, we give three assumptions for numerical label of multi-label instance in this paper. Based on this, we propose an effective multi-label learning framework called MLL-LE, ie. Multi-Label Learning with Label Enhancement, which incorporates the regression loss and the three assumptions into a unified framework. Extensive experiments validate the effectiveness of MLL-LE framework.