Joint Discriminative Distribution Adaptation and Manifold Regularization for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) has become a basic technology for cross-domain recognition and has received extensive attention in recent years. UDA aims to obtain a classifier for the target domain by learning source instances with different data distributions. However, traditional domain adaptation algorithms cannot effectively explore the manifold structure of data while reducing the distribution differences between domains. To address this problem, this paper proposes a new UDA framework called Joint Discriminative Distribution Adaptation and Manifold Regularization (DDAMR). DDAMR makes full use of the category information and geometric structure of samples in the Grassmann manifold to learn the domain-invariant classifier. Specifically, DDAMR performs discriminative distribution adaptation during dynamic distribution calibration to enhance the discrimination ability of the feature space. In addition, DDAMR introduces manifold regularization that can maintain the proximity relationship of the samples. It can maximize effectively the consistency between the prediction structure of the domain-invariant classifier f and the inherent manifold structure of the sample. A large number of results from cross-domain experiments have demonstrated the effectiveness of our DDAMR algorithm.