Multi-metric domain adaptation for unsupervised transfer learning

Unsupervised domain adaptation aims to learn a classifier for the unlabelled target domain by leveraging knowledge from a labelled source domain. This study presents a novel domain adaptation framework from global and local transfer perspectives, referred to as multi-metric domain adaptation (MMDA) for unsupervised transfer learning. At the global level, MMDA minimises the marginal and within-class distances and maximises the between-class distance between domains while maintaining the features of the source domain to improve the cross-domain adaptability. At the local level, MMDA exploits both in- and cross-domain manifold structures embedded in data samples to increase the discriminative ability. The authors learn a coupled transformation that projects the source and target domain data onto respective subspace where the statistical and geometrical divergences are reduced simultaneously. They formulate global and local adaptation methods in an optimisation problem and derive an analytic solution to the objective function. Extensive experiments demonstrate that MMDA shows improvements in classification accuracy compared with several existing state-of-the-art methods.