李雅倩2,,,
张亚茹1,
刘彬1
1.燕山大学信息科学与工程学院 秦皇岛 066004
2.燕山大学工业计算机控制工程河北省重点实验室 秦皇岛 066004
基金项目:国家自然科学基金(51641609)
详细信息
作者简介:吴超：男，1990年生，博士生，研究方向为计算机视觉
李雅倩：女，1982年生，副教授，研究方向为计算机视觉
张亚茹：女，1995年生，博士生，研究方向为计算机视觉
刘彬：男，1953年生，教授，研究方向为计算机视觉
通讯作者:李雅倩 yaqianli@126.com
中图分类号:TP391计量
文章访问数:1536
HTML全文浏览量:694
PDF下载量:76
被引次数:0
出版历程
收稿日期:2019-03-27
修回日期:2019-09-03
网络出版日期:2019-09-12
刊出日期:2020-02-19
Correntropy-based Fusion Extreme Learning Machine forRepresentation Level Feature Fusion and Classification
Chao WU1,Yaqian LI2,,,
Yaru ZHANG1,
Bin LIU1
1. School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2. Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University,Qinhuangdao 066004, China
Funds:The National Natural Science Foundation of China (51641609)
摘要
摘要:在极限学习机(ELM)网络结构和训练模式的基础上，该文提出了相关熵融合极限学习机(CF-ELM)。针对多数分类方法中表示级特征融合不充分的问题，该文将核映射与系数加权相结合，提出了能够有效融合表示级特征的融合极限学习机(F-ELM)。在此基础上，用相关熵损失函数替代均方误差(MSE)损失函数，推导出用于训练F-ELM各层权重矩阵的相关熵循环更新公式，以增强其分类能力与鲁棒性。为了检验方法的可行性，该文分别在数据库Caltech 101, MSRC和15 Scene上进行实验。实验结果证明，该文所提CF-ELM能够在原有基础上进一步融合表示级特征，从而提高分类正确率。
关键词:极限学习机/
表示级特征融合/
相关熵/
分类
Abstract:Based on the network structure and training methods of the Extreme Learning Machine (ELM), Correntropy-based Fusion Extreme Learning Machine (CF-ELM) is proposed. Considering the problem that the fusion of representation level features is insufficient in most classification methods, the kernel mapping and coefficient weighting are combined to propose a Fusion Extreme Learning Machine (F-ELM), which can effectively fuse the representation level features. On this basis, the Mean Square Error (MSE) loss function is replaced by the correntropy-based loss function. A correntropy-based cycle update formula for training the weight matrices of the F-ELM is derived to enhance classification ability and robustness. Extensive experiments are performed on Caltech 101, MSRC and 15 Scene datasets respectively. The experimental results show that CF-ELM can further fuse the representation level features to improve the classification accuracy.
Key words:Extreme Learning Machine (ELM)/
Representation level feature fusion/
Correntropy/
Classification
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