山西大学学报（自然科学版）2022，Vol.45Issue(1)：60-67,8.DOI:10.13451/j.sxu.ns.2021057
李群特征的深度学习算法研究
Research on Deep Learning Algorithm for Lie Group Features
摘要
Abstract
Since complex data in the real world can often be represented as Lie group structures, this paper designs a deep network ar-chitecture with Lie group features as input, so as to make use of the powerful feature representation ability of deep learning for pat-tern recognition and other tasks. In the process of constructing Lie group deep neural network, in order to ensure that Lie group fea-tures can be restricted to the structure of differential manifold during optimization, a deep learning algorithm suitable for Lie group features was introduced. In the process of feature learning, the algorithm can not only ensure that the information of data manifold structure is not lost, but also limit the assumption space of parameter optimization. The deep learning algorithm based on Lie group features is applied to CIFAR-BW and MNIST data sets, and the Lie group features of the spoke models are designed for static imag-es. The experimental results show that the proposed algorithm can converge to a more ideal result in fewer iterations.关键词
李群特征/深度学习/辐条模型/图像分类/表示学习Key words
Lie group features/deep learning/spoke model/image classification/representation learning分类
信息技术与安全科学引用本文复制引用
杨梦铎,李凡长..李群特征的深度学习算法研究[J].山西大学学报（自然科学版）,2022,45(1):60-67,8.基金项目
江苏省高等学校自然科学研究项目(20KJB520014) （20KJB520014）
苏州经贸职业技术学院自然科学研究项目(YJ-ZK2010) （YJ-ZK2010）
