Study of feature vector discriminability optimization for classification based on PCA and MDA

被引：0
|
作者：
Jiang Xiangdong [1 ,2 ]
Tang Jiansheng [2 ]
Xiao Jigang [2 ]
Jin, Yunji [2 ]
Zou Jinshun [2 ]
机构：
[1] Harbin Engn Univ, Harbin, Heilongjiang, Peoples R China
[2] Sci & Technol Underwater Acoust Antagonizing Lab, Beijing, Peoples R China
来源：
PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS RESEARCH AND MECHATRONICS ENGINEERING | 2015年 / 121卷
关键词：
acoustic classification; feature extraction; linear discriminant analysis;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
To solve the problem of weak discriminability of the feature vector for underwater acoustic classification, a method of feature differentiation optimization based on PCA and MDA analysis was proposed in this paper. It can enhance the differentiation by optimal mapping the feature vectors to transform space. The data processing results proved the method is feasible and has the advantage of feature dimension reducing that is useful in practice.
引用
收藏
页码：1759 / 1763
页数：5
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