Research and Application of Non-negative Matrix Factorization with Sparseness Constraint in Recognition of Traditional Chinese Medicine Pulse Condition

被引：0
|
作者：
Guo Rui [1 ]
Wang Yiqin [1 ]
Yan Haixia [1 ]
Li Fufeng [1 ]
Xu Zhaoxia [1 ]
Yan Jianjun [2 ]
机构：
[1] Shanghai Univ Tradit Chinese Med, Lab Informat Access & Synth TCM Diag 4, Shanghai, Peoples R China
[2] East China Univ Sci & Technol, Ctr Mechatron Engn, Shanghai, Peoples R China
来源：
2010 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS (BIBMW) | 2010年
基金：
中国国家自然科学基金;
关键词：
Traditional Chinese Medicine; pulse condition; non-negative matrix factorization with sparseness constraint; feature extraction and recognition of pulse; support vector machine;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
n this paper, the recognition method based on non-negative matrix factorization with sparseness constraint (NMFs) combined with the support vector machine (SVM) was proposed to identify the type of the common pulse condition of Chinese Traditional Medicine (TCM). First, pulse data were factorized by NMFs to obtain projection coefficients as training sample set to build recognition mode with SVM. Then the method proposed was compared with the classical time-domain method of pulse feature extraction. And time-domain features were extracted to identify the type of pulse with the same SVM classifier. Finally, the results showed that projection coefficients obtained by NMFs more use of recognition of TCM pulse.
引用
收藏
页码：682 / 685
页数：4
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