通信学报2016，Vol.37Issue(11)：57-67,11.DOI:10.11959/j.issn.1000-436x.2016213
正则化流形信息极端学习机
Regularized manifold information extreme learning machine
摘要
Abstract
By exploiting the thought of manifold learning and its theoretical method, a regularized manifold information ex-treme learning machine algorithm aimed to depict and fully utilize manifold information was proposed. The proposed algo-rithm exploited the geometry and discrimination manifold information of data to perform network of ELM. The proposed algorithm could overcome the problem of the overlap of information. Singular problems of inter-class and within-class were solved effectively by using maximum margin criterion. The problem of inadequate learning with limited samples was solved. In order to demonstrate the effectiveness, comparative experiments with ELM and the related update algorithms RAFELM, GELM were conducted using the commonly used image data. Experimental results show that the proposed algorithm can significantly improve the generalization performance of ELM and outperforms the related update algorithms.关键词
极端学习机/几何结构/流形信息/机器学习Key words
extreme learning machine/geometry/manifold information/machine learning分类
信息技术与安全科学引用本文复制引用
刘德山,楚永贺,闫德勤..正则化流形信息极端学习机[J].通信学报,2016,37(11):57-67,11.基金项目
国家自然科学基金资助项目(No.61105085)；辽宁省教育厅基金资助项目（No.L2014427）Foundation Items:The National Natural Science Foundation of China (No.61105085), Liaoning Provincial Department of Educa-tion Project(No.L2014427) （No.61105085）
