Improving multi-label learning by modeling Local label and feature correlations

Multi-label learning deals with the problem that each instance is associated with multiple labels simultaneously, and many methods have been proposed by modeling label correlations in a global way to improve the performance of multi-label learning. However, the local label correlations and the influence of feature correlations are not fully exploited for multi-label learning. In real applications, different examples may share different label correlations, and similarly, different feature correlations are also shared by different data subsets. In this paper, a method is proposed for multi-label learning by modeling local label correlations and local feature correlations. Specifically, the data set is first divided into several subsets by a clustering method. Then, the local label and feature correlations, and the multi-label classifiers are modeled based on each data subset respectively. In addition, a novel regularization is proposed to model the consistency between classifiers corresponding to different data subsets. Experimental results on twelve real-word multi-label data sets demonstrate the effectiveness of the proposed method.