Multi-Label Learning with Local Similarity of Samples

Multi-label learning has been successfully applied to solve instance multi-semantics problems. Moreover, the topology information of samples is often adopted in existing works to improve the prediction performance, in which the similarity of samples is usually calculated in the entire feature space. However, in real-world applications, each label is often determined by a subset of the original features, so when we focus on different labels, the similarity of two instances may be different. In this paper, we propose a multi-label learning method by exploiting the local similarity of samples. Specifically, the smoothness assumption is applied to assume that if the feature subset is similar between samples, the corresponding label should be similar. In addition, L1 regularization is also adopted to sparse the weight coefficients when constraining the output space of the instance. The experimental results on several data sets validate the effectiveness of the proposed method.