Unsupervised Heterogeneous Domain Adaptation with Sparse Feature Transformation
Proceedings of The 10th Asian Conference on Machine Learning, PMLR 95:375-390, 2018.
Heterogeneous domain adaptation (HDA), which aims to adapt information across domains with different input feature spaces, has attracted a lot of attention recently. However, many existing HDA approaches rely on labeled data in the target domain, which is either scarce or even absent in many tasks. In this paper, we propose a novel unsupervised heterogeneous domain adaptation approach to bridge the representation gap between the source and target domains. The proposed method learns a sparse feature transformation function based on the data in both the source and target domains and a small number of existing parallel instances. The learning problem is formulated as a sparsity regularized optimization problem and an ADMM algorithm is developed to solve it. We conduct experiments on several real-world domain adaptation datasets and the experimental results validate the advantages of the proposed method over existing unsupervised heterogeneous domain adaptation approaches.