Domain Adaptive BLS Model Based on Manifold Regularization Framework and MMD
As an efficient incremental learning system based on random vector function-link network(RVFLN),broad learning system(BLS)has the characteristics of fast adaptive model structure selection and high precision.However,due to the lack of label data in target classification,the traditional BLS is difficult to improve the classi-fication effect of target domain by using relevant domain knowledge.Therefore,a domain adaptive BLS(DABLS)model based on manifold regularization framework and maximum mean discrepancy(MMD)is developed to achieve cross-domain image classification of target domain under unlabeled condition.Firstly,the feature nodes and en-hancement nodes of BLS are constructed to effectively extract features from the data of source domain and target domain.The manifold regularization framework is used to construct Laplacian matrix in order to explore the mani-fold characteristics of the target domain data and mine the potential information of the target domain data.Then the transfer learning method is used to construct the MMD penalty term between the source domain data and the target domain data to match the projection mean between the source domain and the target domain.The feature nodes,enhancement nodes,MMD penalty term and Laplacian matrix are combined to construct the objective func-tion.Ridge regression analysis is used to solve the objective function to obtain the output coefficients,so as to im-prove the cross-domain classification performance.Finally,a large number of validation and comparative experi-ments are carried out on different image data sets,and the experiment results show that the DABLS can better achieve cross-domain classification on different image data sets,and has strong generalization ability and better sta-bility.
Broad learning system(BLS)manifold regularization frameworkmaximum mean discrepancy(MMD)domain adaptationimage classification
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