Research on unlabeled target domain adaptive information processing based on TrAdaBoost algorithm

In recent years, information processing technology based on transfer learning has developed rapidly, and more and more data users do have not enough time to complete data labeling. Domain Adaptation (DA) information processing for unlabeled data is becoming increasingly important. To improve the performance of the model in the target domain information processing, we design a target domain information network and propose a collaborative learning model between the private network and the regional adaptive network based on the TrAdaBoost algorithm. Different from the traditional method, this model can avoid directly reducing the difference between domains as the model optimization goal, and the target is optimized for cluster regularization, driving the target domain data points closer to the cluster center. And further, promote the training of domain information networks. Through simulation experiments, it is concluded that under this model, the target domain private network is effectively trained based on the TrAdaBoost algorithm under the specified target domain, to achieve better information processing performance in the target domain.