A relative labeling importance estimation algorithm based on global-local label correlations for multi-label learning

Yilu Liu, Fuyuan Cao

Abstract: In multi-label learning, considering the relative importance between labels can yield better performance than considering the equal importance. To explore relative labeling importance, many existing algorithms introduce the global label correlations. However, the global correlations can only reflect the semantic relation between labels, while ignoring the label correlation differences between different instances. In practical applications, labels with high semantic relevance may not be highly relevant in all instances. In this paper, we consider both global and local label correlations to estimate relative labeling importance. Firstly, we calculate a global label correlation matrix in the whole label space. Secondly, each instance subset is assigned a local label correlation matrix, which is learned from the cosine similarity of labels within the cluster. Based on the assumption that label correlations can be transferred from the original categorical space to the numerical label space, we add global and local label correlation regularization terms. Finally, we integrate the importance estimating and the model training into a unified framework, and propose an alternative optimization algorithm to solve it. To validate the effectiveness of the proposed algorithm, we conduct experiments on thirteen multi-label datasets. Experimental results show that the proposed algorithm outperforms existing multi-label learning algorithms.