两阶段领域自适应学习

针对领域自适应问题中源域和目标域的联合分布差异最小化问题,提出两阶段领域自适应学习方法.在第一阶段考虑样本标签和数据结构的判别信息,通过学习一个共享投影变换,使投影后的共享空间中边缘分布的差异最小.第二阶段利用源域标记数据和目标域非标记数据学习一个带结构风险的自适应分类器,不仅能最小化源域和目标域条件分布差异,还能进一步保持源域和目标域边缘分布的流形一致性.在3个基准数据集上的实验表明,文中方法在平均分类准确率和Kappa系数两项评价指标上均表现较优.

Two Stage Domain Adaptation Learning

Aiming at minimization of the joint distribution difference between source domain and target domain in domain adaptation, a two-stage domain adaptation learning method is proposed. In the first stage, the discriminative information of sample labels and the data structure are considered, and a shared projection transformation is learned to minimize the difference of marginal distribution in the shared-projected space. In the second stage, an adaptive classifier with structural risk is learned by the labeled source data and unlabeled target data. The classifier minimizes the difference of conditional distribution of source domain and target domain as well as maintains the manifold consistency underlying the marginal distributions. Experiments on three benchmark datasets show that the method achieves better results on average classification accuracy and the Kappa coefficient.