Estimating Latent Relative Labeling Importances for Multi-Label Learning

In multi-label learning, each instance is associated with multiple labels simultaneously. Most of the existing approaches directly treat each label in a crisp manner, i.e. one class label is either relevant or irrelevant to the instance. However, the latent relative importance of each relevant label is regrettably ignored. In this paper, we propose a novel multi-label learning approach that aims to estimate the latent labeling importances while training the inductive model simultaneously. Specifically, we present a biconvex formulation with both instance and label graph regularization, and solve this problem using an alternating way. On the one hand, the inductive model is trained by minimizing the least squares loss of fitting the latent relative labeling importances. On the other hand, the latent relative labeling importances are estimated by the modeling outputs via a specially constrained label propagation procedure. Through the mutual adaption of the inductive model training and the specially constrained label propagation, an effective multi-label learning model is therefore built by optimally estimating the latent relative labeling importances. Extensive experimental results clearly show the effectiveness of the proposed approach.