Partial label learning via low-rank representation and label propagation

Abstract

In partial label learning, each training instance is assigned with a set of candidate labels, among which only one is correct. An intuitive strategy to learn from such ambiguous data is disambiguation. Existing methods following such strategy either identify the ground-truth label via treating each candidate label equally or disambiguate the candidate label set via assuming latent variable and optimizing it iteratively. In this paper, we propose a novel two-stage method called partial label learning via low-rank representation and label propagation, where instance similarity and label confidence are taken into consideration to improve the disambiguation ability of the model. In the first stage, we first build the global instance–similarity relationship via low rank representation and sparse constraint and then obtain the accurate instance-label assignments via iterative label propagation strategy. In the second stage, we utilize the Adaboost.R2 to make prediction for unseen instances, where CART is incorporated as the weak classifier. Extensive experiments on the artificial and real-world data sets demonstrate that the proposed method can achieve superior or comparable performance than the comparing state-of-the-art approaches.