Geometric Knowledge Embedding for unsupervised domain adaptation
作者：
Highlights：
• We exploit the geometric information of the source and target data to learn discriminative representations. In this sense, the features of each sample and its neighbors are both considered during the learning procedure.
• We introduce MMD into the graph convolutional network to explore geometric knowledge for learning transferable embeddings.
• GCN has not been applied to domain adaptation problems before, and this makes our proposed method a decent supplement to existing domain adaptation approaches.
• We conduct comprehensive experiments on four real-world applications, including object recognition, image classification and text categorization, to demonstrate the effectiveness of our proposed method.
摘要
•We exploit the geometric information of the source and target data to learn discriminative representations. In this sense, the features of each sample and its neighbors are both considered during the learning procedure.•We introduce MMD into the graph convolutional network to explore geometric knowledge for learning transferable embeddings.•GCN has not been applied to domain adaptation problems before, and this makes our proposed method a decent supplement to existing domain adaptation approaches.•We conduct comprehensive experiments on four real-world applications, including object recognition, image classification and text categorization, to demonstrate the effectiveness of our proposed method.
论文关键词：Domain adaptation,Graph-based model,Geometric knowledge,Graph convolutional network,Maximum Mean Discrepancy
论文评审过程：Received 18 July 2019, Revised 19 October 2019, Accepted 22 October 2019, Available online 25 October 2019, Version of Record 8 February 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2019.105155
