Feature-Induced Labeling Information Enrichment for Multi-Label Learning
In multi-label learning, each training example is represented by a single instance (feature vector) while associated with multiple class labels simultaneously. The task is to learn a predictive model from the training examples which can assign a set of proper labels for the unseen instance. Most existing approaches make use of multi-label training examples by exploiting their labeling information in a crisp manner, i.e. one class label is either fully relevant or irrelevant to the instance. In this paper, a novel multi-label learning approach is proposed which aims to enrich the labeling information by leveraging the structural information in feature space. Firstly, the underlying structure of feature space is characterized by conducting sparse reconstruction among the training examples. Secondly, the reconstruction information is conveyed from feature space to label space so as to enrich the original categorical labels into numerical ones. Thirdly, the multi-label predictive model is induced by learning from training examples with enriched labeling information. Extensive experiments on fifteen benchmark data sets clearly validate the effectiveness of the proposed feature-induced strategy for enhancing labeling information of multi-label examples.