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.

中文翻译：

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用于域适应的潜在空间搜索方法

在传统的机器学习中，源域和目标域之间的数据分布通常存在差异。域适应（DA）被提出来通过使用来自不同源域的知识来学习目标域的鲁棒分类器。大多数 DA 方法仅关注数据的几何结构或统计属性，以减少域之间的差异。忽略了这两方面的互补性，在一定程度上造成了领域适应不足的问题。在本文中，我们提出了考虑几何和统计特性的域适应的潜在空间搜索（LSS）方法。 LSS由两部分组成：潜在子空间学习和空间搜索子空间学习。在潜在子空间中，数据的几何和统计属性通过低秩耦合投影以及数据的联合分布来保留。对于该解决方案，使用迭代反馈方法来获得稳健的子空间。此外，为了提高子空间的可区分性，采用空间搜索优化算法重建潜在子空间，使得源域和目标域在子空间中能够很好地交错。一项比较研究表明，所提出的 LSS 比文献中报道的其他最先进模型具有更好的性能。