Partial label learning with heterogeneous domain adaptation

Abstract

Partial label learning (PLL) seeks a classification model with partially labeled (PL) instances. Each PL instance is attached with a candidate label set, with only one being ground-truth. A fundamental assumption of the previous PLL works is that there are sufficient PL instances in the learning process. Nevertheless, this assumption may not always be valid. In real-world scenarios, there may be only a few PL instances available for training. To this end, we introduce a new PLL method with heterogeneous domain adaptation (PLL-HDA). PLL-HDA leverages the knowledge from the source domain to induce a PLL classifier of the target domain, which contains only a few PL instances for training. Firstly, PLL-HDA introduces a common subspace to measure the instances from the source and target domains, where the instances from these domains are described by heterogeneous features of distinct dimensions. Secondly, we augment the features of the common subspace by merging the original features from both domains. Thirdly, a large-margin-based PLL learning system is established on the augmented features. It leads to a simplified dual form, which can be directly resolved by off-the-shelf support vector machine solvers. Lastly, a heuristic framework is proposed to resolve the PLL-HDA classification model. In this framework, the tasks of learning the classifier on the augmented features and identifying the ground-truth labels of target domain are conducted alternately. Extensive experiments on both the controlled PLL and real-world PLL datasets illustrate the superiority of PLL-HDA over the existing PLL methods. Our code is available at: https://github.com/Sux86.