Study of feature vector discriminability optimization for classification based on PCA and MDA
Authors
Xiangdong Jiang, Jiansheng Tang, Jigang Xiao, Yunji Jin, Jinshun Zou
Corresponding Author
Xiangdong Jiang
Available Online April 2015.
- DOI
- 10.2991/isrme-15.2015.357How to use a DOI?
- Keywords
- acoustic classification; feature extraction; linear discriminant analysis
- Abstract
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.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xiangdong Jiang AU - Jiansheng Tang AU - Jigang Xiao AU - Yunji Jin AU - Jinshun Zou PY - 2015/04 DA - 2015/04 TI - Study of feature vector discriminability optimization for classification based on PCA and MDA BT - Proceedings of the 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering PB - Atlantis Press SP - 1759 EP - 1763 SN - 1951-6851 UR - https://doi.org/10.2991/isrme-15.2015.357 DO - 10.2991/isrme-15.2015.357 ID - Jiang2015/04 ER -