Multi-label learning with discriminative features for each label

• This paper proposes a novel multi-label learning algorithm, Multi-label Learning with Discriminative Features for each Label (ML-DFL), based on binary relevance by exploiting the correlations between positive and negative instances for each label.
• To investigate the correlations as stated in Point 1, we propose a new spectral clustering algorithm Spectral Instance Alignment (SIA).
• Substantial experiments across six data sets from three different application domains validate the superiority of our proposed ML-DFL to five competitors.

During the last decade, multi-label learning has attracted the attention of more and more researchers in machine learning field due to wide real-world applications. Existing approaches often predict an unseen example for all labels based on the same feature vector. However, this strategy might be suboptimal since different labels usually depend on different aspects of the feature vector. Furthermore, for each label there is close relationship between positive and negative instances, which is quite informative for classification. In this paper, we propose a new algorithm called ML-DFL, which trains a model for each label with newly constructed discriminative features. In order to form these features, we also propose a spectral clustering algorithm SIA to find the closely located local structures between positive and negative instances, which are assumed to be of more discriminative information, and then transform the original data set by consulting the clustering results in a simple but effective way. Comprehensive experiments are conducted on a collection of benchmark data sets. The results clearly validate the superiority of ML-DFL to various competitors.