PP-PLL: Probability Propagation for Partial Label Learning

Abstract

Partial label learning (PLL) is a weakly supervised learning framework which learns from the data where each example is associated with a set of candidate labels, among which only one is correct. Most existing approaches are based on the disambiguation strategy, which either identifies the valid label iteratively or treats each candidate label equally based on the averaging strategy. In both cases, the disambiguation strategy shares a common shortcoming that the ground-truth label may be overwhelmed by the false positive candidate labels, especially when the number of candidate labels becomes large. In this paper, a probability propagation method for partial label learning (PP-PLL) is proposed. Specifically, based on the manifold assumption, a biconvex regular function is proposed to model the linear mapping relationships between input features and output true labels. In PP-PLL, the topological relations among training samples are used as additional information to strengthen the mutual exclusiveness among candidate labels, which helps to prevent the ground-truth label from being overwhelmed by a large number of candidate labels. Experimental studies on both artificial and real-world data sets demonstrate that the proposed PP-PLL method can achieve superior or comparable performance against the state-of-the-art methods.