Indirect Category Data Transfer Learning Algorithm using Regularization Discrimination

To deal with a large amount of redundant data in the indirect category database and inefficient redundancy elimination of the existing methods, we proposed an indirect category data transfer learning algorithm based on regularization discrimination. First of all, we denoised indirect category data, calculated the objective function of distance between the source domain and the target domain, and established the transfer relationship between indirect category data. Second, we adopted the regularization discriminant technique to divide the transfer network structure of indirect category data into five modules, analyzed the effects and advantages of different modules, and constructed the transfer network structure of indirect category data. Finally, the indirect category data transfer was realized by the design of the indirect category data transfer learning algorithm. The results show that the proposed algorithm can effectively eliminate redundancy of indirect category data, the amplitude of fluctuation of indirect category data is small, the transfer time and energy consumption of the algorithm are low, and the accuracy is as high as about 90%, which indicates that the proposed algorithm is far superior to the traditional method and has high application value.