Partial Label Learning with competitive learning graph neural network

Abstract

Partial Label Learning (PLL) is a weakly supervised learning framework where each instance may be associated with more than one candidate label, among which only one is true. Traditionally, the PLL problem is solved by removing the false candidate labels based on the instance relationship, while the potentially useful information between instances and labels as well as the potential candidate label relationship is ignored. In this paper, a new PLL framework PL-CGNN is proposed, which treats the instances with false labels as noise, and the PLL is reformulated to remove the noise instances. First of all, the feature of each label class is approximately represented by the center point of all the related instances. The significant operation enables the similarity between instances and labels measurable. Next, all the candidate labels for each instance compete for the biggest similarity. To further improve the robustness of the model, the competition procedure for the most similar label is extended to the neighbors of this instance. The label with the most wins is the final ground-truth one. The relationship between candidate labels guides the situation that the competition process develops into. Through iterative competitive learning, each label class approaches the true value. Experiments carried out on diverse datasets show that the performance of the PL-CGNN model is outstanding.