Iterative landmark selection and subspace alignment for unsupervised domain adaptation

Domain adaptation (DA) solves a learning problem in a target domain by utilizing the training data in a different but related source domain, when the two domains have the same feature space and label space but different distributions. An unsupervised DA approach based on iterative landmark selection and subspace alignment (SA) is proposed. The proposed method automatically selects source landmarks from the source domain and iteratively selects target landmarks from the target domain. These well-selected landmarks accurately reflect the similarity between the two domains and are applied to kernel projection of both source and target samples onto a common subspace, where SA is performed. In each iteration, target labels are updated by a classifier that is retrained with the source samples aligned with the target domain. Thus, the distribution of the selected target landmarks gradually approximates the distribution of the source domain. During landmark selection, the quadratic optimization functions are constrained such that the proportions of selected samples per class remain the same as in the original domain, which makes the problem easy to solve and avoids setting hyperparameters. Comprehensive experimental results show that the proposed method is effective and outperforms state-of-theart adaptation methods.