基于流形结构的多源自适应迁移学习算法及应用研究

迁移学习是将源域的知识迁移解决目标域问题的方法,能有效解决数据分布不一致的问题.针对多源域迁移时传统方法缺乏对多源域的可迁移性的合理分析和迁移效果的有效处理问题,提出一种基于流形结构的多源自适应迁移学习的方法,旨在提高单源域迁移效果的同时实现多源域的有效迁移.首先,对多源域进行可迁移性分析,选择可迁移的源域;然后,适配边缘分布和条件分布并引入均衡因子得到均衡分布适配,同时利用流形正则化约束数据结构,使单源域的信息使用最大化;最后,通过加权因子对不同源域分类器进行自适应加权,充分利用多源域的信息求解目标域问题.将该算法应用于滚磨光整加工中滚抛磨块的优选,通过建立滚抛磨块的相似度匹配方法,构建基于流形结构的多源自适应迁移学习的滚抛磨块优选模型.大量对比实验表明所提出方法表现更佳,准确率最高至73.44%,可以为滚磨光整中滚抛磨块的选择提供更有效的决策指导.

Research on multi-domain adaptation-manifold regularization and application

Transfer learning is a method of transferring knowledge from the source domain to solve the problem of the target domain, which can effectively solve the problem of inconsistent data distribution. Aiming at the problem that traditional methods lack a reasonable analysis of the transferability of multi-domain and the effective treatment of transfer effects in multi-domain transfer, the multi domain adaptation-manifold Regularization transfer learning algorithm is proposed to improve the effect of single source domain transfer while realizing effective transfer of multi-domain. Firstly, the transferability of multi-domain is analysed to select the source domains that can be transferred. Then, the marginal distribution and the conditional distribution is adapted and the balance factor is introduced to obtain the balanced distribution adaptation, and the manifold regularization is used to constrain the data structure to maximize the use of information in the single source domain. Finally, different domain classifiers are adaptively weighted by weighting factors, and the information of multi-domain is fully utilized to solve the target domain problem. This algorithm is applied to the optimization of the abrasive media in the mass finishing. By establishing the similarity matching method of the abrasive media, the multi-domain adaptation-manifold regularization of the abrasive media optimization model is constructed. A large number of comparative experiments show that the proposed method performs better, with an accuracy rate of up to 73.44%, which can provide more effective decision-making guidance for the optimization of the abrasive media in the mass finishing.