联合类间及域间分布适配的迁移学习方法

在域间分布适配的过程中,容易丢失一些重要的域自身信息,在源域上难以训练获得一个有效的分类器,影响其在目标域上的泛化与标注性能.基于此种情况,文中提出联合类间及域间分布适配的迁移学习方法.通过学习一个公共投影矩阵,分别将源域与目标域映射到一个公共子空间上.采用最大均值差异方法分别度量类间及域间分布距离.在目标函数的优化过程中,不但显式地使域间分布差异变小,而且增大不同类别间的差异性,提高源域与目标域之间知识迁移的性能.在迁移学习数据集上的实验表明文中方法的有效性.

Transfer Learning with Joint Inter-class and Inter-domain Distributional Adaptation

Inter-domain information is lost during the process of inter-domain distributional adaptation.Therefore,it is difficult to train an effective classifier in the source domain,and the performance of generalization and tagging in the target domain are affected.Aiming at this problem,an approach,joint inter-class and inter-domain distributional adaptation for transfer learning,is proposed to address this challenge.The proposed method is formulated by learning a projection matrix to map new representations of respective domains into a common subspace.And the distance-measure method of the maximum mean discrepancy is adopted to compute the distance of inter-class and inter-domain distributions.During the optimization procedure,the inter-domain distributional difference is reduced explicitly,and the inter-class distributional difference is enlarged greatly.The capability of knowledge transfer between different domains is improved.Experiments on transfer learning dataset verify the effectiveness of the proposed approach.