Recognition Model Based Feature Extraction and Kernel Extreme Learning Machine for High Dimensional Data
Recognition Model Based Feature Extraction and Kernel Extreme Learning Machine for High Dimensional Data
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DOI：
10.4028/www.scientific.net/amr.875-877.2020
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发表时间：
2014-02
期刊：
影响因子：
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通讯作者：
Yan Shi;L. J. Zhao;Jian Tang
中科院分区：
文献类型：
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作者：
Yan Shi;L. J. Zhao;Jian Tang
在疾病诊断和产品质量监测中，常常需要使用质谱、近红外光谱等高维数据。针对特征提取的非线性和学习速度慢的问题，提出了一种主成分分析（PCA）和核极端学习机（KELM）相结合的建模方法。利用PCA算法提取的特征输入到基于非线性分类的KELM中，学习速度快。根据分类性能选择特征的数目。基于基准测试中的质谱数据的实验结果表明，该方法具有更好的性能。该方法也可用于基于雷达数据的目标识别。
High dimensional data such as mass-spectrometric and near-infrared spectrum are always used in disease diagnosis and product quality monitoring. Aim at the nonlinear feature extraction and low learning speed problems, a novel modeling approach combined principal component analysis (PCA) with kernel extreme learning machine (KELM) is proposed. The extracted features using PCA algorithms are fed into nonlinear classification based KELM with fast learning speed. The numbers of the features are selected according the classification performance. The experimental results based on the mass-spectrometric data in the benchmark demonstrate that the proposed approach has better performance. This approach can also be used to target recognition based on radar data.