A Semisupervised Domain Adaptation Framework Using Dynamic Distribution Alignment and Manifold Regularization

Summary

This paper presents SDM, a semi-supervised domain adaptation method that builds a classifier for a target domain with some unlabelled data by using a source domain with abundant labelled data. It starts from an SVM trained on source data, then iteratively adds target data and deletes source data while minimising structural risk and joint distributions and maximising manifold consistency. The authors report that SDM outperforms fifteen state-of-the-art methods on seven public datasets, including under noisy and missing-value conditions.