Weakly-supervised Learning with Partial Multi-Labels by Leveraging Dual Label Correlation Perspectives

Multi-Label Learning (MLL) refers to inducing multi-label prediction models from the precisely labeled training dataset. However, in many real-world scenarios, e.g ., crowdsourcing annotations, the training datasets are often only partially valid, where each training instance is associated with a candidate label set, covering ground-truth labels but also with irrelevant ones. Naturally, learning with such datasets, formally referred to as Partial Multi-label Learning (PML), involves many noisy supervised signals, hence imposing a significant challenge to the prediction model induction. To meet this challenge, we purify the noisy supervised signals by formulating the latent label distribution, i.e ., the probability of a candidate label being a ground-truth one, and then jointly learn it with the prediction model by minimizing their regularized Wasserstein distance, i.e ., a robust distance for distributions as well as involving label correlations. Therefore, we propose a novel PML method, namely Wasserstein Partial Multi-Label Learning with dual Label Correlation Perspectives (

), solved by the gradient descent with an augmented Lagrange multiplier technique. To further enhance the robustness of against exceptionally high ratios of irrelevant labels, we extend it with a Dual-branch Competitive Cleansing mechanism, leading to -D. Besides, we also analyze the generalization error bound and time complexity of and -D. The extensive experiments are constructed by comparing and -D with existing PML baselines across synthetic and real-world datasets, and empirical results demonstrate that and -D can outperform the PML baselines in various noisy levels.