A new transfer learning-based method for label proportions problem

Abstract

Learning with label proportions (LLP), which only provides the unlabeled instances in the bag and the bag’s label proportion, has been widely studied recently. However, most of the existing LLP methods do not consider the knowledge transfer from the source task to the target task. In addition, in the process of the collecting data, the data may be corrupted by noise, and this always leads to the uncertain data in its representation. This paper proposes a new transfer learning-based approach for the problem of learning with label proportions, which is called TL-LLP in brief, to transfer knowledge from the source task to the target task where both the source and target tasks contain uncertain data. We first formulate the objective model to deal with transfer learning and uncertain data for the label proportions problem at the same time. We then propose an iterative framework to solve the proposed objective model and obtain the accurate classifier for the target task. Extensive experiments have shown that the proposed TL-LLP method can obtain better performance and is less sensitive to noise compared with the existing LLP methods.