Latent space search approach for domain adaptation

In traditional machine learning, there is often a discrepancy in data distribution between the source and target domains. Domain adaptation (DA) was proposed to learn the robust classifier for target domain by using knowledge from different source domains. Most DA methods focus on only the geometric structure of the data or statistical properties to reduce the differences between domains. The complementarity of these two aspects is ignored, which causes the problem of domain underadaptation to some degree. In this paper, we propose latent space search (LSS) approach for domain adaptation that consider both geometric and statistical properties. LSS consists of two parts: latent subspace learning and space search subspace learning. In the latent subspace, the geometric and statistical properties of the data are preserved by a low-rank coupled projection as well as joint distribution of the data. For the solution, an iterative feedback approach is used to obtain a robust subspace. Furthermore, to improve the discriminability of the subspace, the space search optimization algorithm is used to reconstruct the latent subspace, so that the source domain and the target domain can interleave well in the subspace. A comparative study illustrates that the proposed LSS has better performance than other state-of-art models reported in the literature.