基于无监督域适应的可区分联合匹配算法

当域之间差异较大时,域适应的迁移效果较差.缩小域差可改善迁移效果,但却忽略后期分类时的可区分性.因此,文中提出基于无监督域适应的可区分联合匹配算法,根据域间类别的不同进行差异化处理,并结合特征匹配和实例重加权提高迁移效果.使用联合概率分布作为域之间数据分布差异的度量,缩小相同类域之间的距离,提高迁移性;扩大不同类域之间的距离,提高区分性.在特征降维的过程中联合特征匹配和实例重加权,共同构造特征变换矩阵.实验表明,文中算法在18组任务上的分类效果较优.

Discriminative Joint Matching for Unsupervised Domain Adaptation

The transfer effect of domain adaption is poor due to the large differences between domains. It can be improved by reducing the domain difference. However, the discriminability of later classification is ignored. A discriminative joint matching algorithm is proposed to handle this problem. Differentiation treatments are conducted according to different categories between domains. Feature matching and instance reweighting are combined to improve the migration effect. The joint probability distribution is employed to measure the difference of data distribution between domains. The transferability is enhanced by reducing the distance between the same domains. The discriminability is improved by expanding the distance between different domains. Feature matching and instance weighting are combined in the process of feature dimensionality reduction to jointly construct a feature transformation matrix. The experimental results show that the classification result of the proposed algorithm on 18 tasks is better.