基于传递式领域自适应的异构样本增强方法OACSTPCD

Small sample problem exists widely in data-driven modeling.Domain adaptation achieves small sample enhancement in target domain by transferring sample knowledge from source domain to target domain.However,those methods are limited in practical application because it is difficult to deal with sample enhancement scenarios with large domain distribution differences.To solve these problems,we propose a heterogeneous sample enhancement method based on transitive domain adaptation.Firstly,a transitive exploration strategy is proposed.A domain distribution exploration strategy for heterogeneous domains is designed based on specific and common features,which effectively alleviates negative transfer and provides support for subsequent distribution matching.Then,a distributed joint matching mechanism is proposed to match the marginal distribution and conditional distribution of heterogeneous domain,and embed an adaptive mechanism to ensure the matching accuracy of heterogeneous domain distribution.The proposed method is verified by the industry-recognized Tennessee-Eastman dataset,and the experimental results show that the proposed method performs better than other methods in heterogeneous domain modeling.