计算机应用与软件2017，Vol.34Issue(4)：169-177,9.DOI:10.3969/j.issn.1000-386x.2017.04.029
基于微分同胚优化极端学习机的人脸识别
FACE RECOGNITION USING OPTIMIZED EXTREME LEARNING MACHINE BASED ON DIFFEOMORPHISM
摘要
Abstract
Extreme learning machine (ELM) has been widely applied in the field of pattern recognition for its efficient and good generalization ability.However, the current ELM and its improved algorithm have not considered the effect of hidden layer nodes' output matrix on the generalization ability of extreme learning machine.Through experiments we find that when the activation function is improperly selected and the data sample dimension is too high, it will result in output value of hidden layer node tending to zero.It comes to make the solution of output weight matrix inaccurate and reduce the classification performance of ELM.In order to solve these problems, an optimized extreme learning machine algorithm based on diffeomorphism is proposed.The algorithm combines techniques of diffeomorphism and dimensionality reduction to improve the robustness of activation functions and overcome the problem that the output value of hidden layer nodes tends to zero.In order to evaluate the validity of the proposed algorithm, face data is used to implement experiments.Experimental results show that the proposed algorithm has a good generalization performance.关键词
极端学习机/激活函数/微分同胚Key words
Extreme learning machine/Activation function/Diffeomorphism分类
信息技术与安全科学引用本文复制引用
李丽娜,闫德勤,楚永贺..基于微分同胚优化极端学习机的人脸识别[J].计算机应用与软件,2017,34(4):169-177,9.基金项目
国家自然科学基金项目(61105085,61373127). （61105085,61373127）
