Multi-label learning with globAl densiTy fusiOn Mapping features

Multi-label learning, where each instance is assigned to multiple categories simultaneously, is a prevalent problem in data analysis. Previous study approaches typically learn from multi-label data by employing the original feature space in the discrimination process of all class labels. However, this traditional strategy might be suboptimal as the original feature space exists irrelevant or redundant information, which affect the performance of classification. In this paper, we propose another strategy to learn from multi-label data, where reconstructed feature space is exploited to improve the classification performance. Accordingly, an intuitive yet effective algorithm named ATOM, i.e. multi-label learning with globAl densiTy fusiOn Mapping features, is proposed. ATOM firstly reconstructs feature spaces specific to each and no label by conducting cluster analysis on its belonging instances, and then utilizes density fusion to excavate optimum centers from the cluster center union, and finally performs classification by querying the reconstructed feature spaces. Comprehensive experimental results on a total of 12 benchmark data sets clearly validate the superiority of ATOM against other competitive algorithms.