Partial Label Learning via Label Influence Function

Abstract

To deal with ambiguities in partial label learning (PLL), state-of-the-art strategies implement disambiguations by identifying the ground-truth label directly from the candidate label set. However, these approaches usually take the label that incurs a minimal loss as the ground-truth label or use the weight to represent which label has a high likelihood to be the ground-truth label. Little work has been done to investigate from the perspective of how a candidate label changing a predictive model. In this paper, inspired by influence function, we develop a novel PLL framework called Partial Label Learning via Label Influence Function (PLL-IF). Moreover, we implement the framework with two specific representative models, an SVM model and a neural network model, which are called PLL-IF+SVM and PLL-IF+NN method respectively. Extensive experiments conducted on various datasets demonstrate the superiorities of the proposed methods in terms of prediction accuracy, which in turn validates the effectiveness of the proposed PLL-IF framework.