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.

中文翻译：

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使用正则化判别的间接类别数据迁移学习算法

针对间接类别数据库中存在大量冗余数据以及现有方法冗余消除效率低下的问题，提出了一种基于正则化判别的间接类别数据迁移学习算法。首先，我们对间接类别数据进行去噪，计算源域和目标域之间的距离目标函数，建立间接类别数据之间的传递关系。其次，我们采用正则化判别技术将间接类别数据的传输网络结构划分为五个模块，分析了不同模块的作用和优势，构建了间接类别数据的传输网络结构。最后，通过设计间接类别数据迁移学习算法实现了间接类别数据迁移。结果表明,所提算法能有效消除间接类别数据的冗余,间接类别数据波动幅度小,算法传输时间和能耗低,准确率高达90%左右,这表明所提算法远远优于传统方法，具有较高的应用价值。