Volume 6, Issue 6, December 2013, Pages 1059 - 1071
Kernel Fisher Discriminant Analysis with Locality Preserving for Feature Extraction and Recognition
- Di Zhang, Jiazhong He, Yun Zhao
- Corresponding Author
- Di Zhang
Available Online 9 January 2017.
- https://doi.org/10.1080/18756891.2013.816051How to use a DOI?
- Kernel-based method, Fisher discriminant analysis, feature extraction, pattern classification
- Many previous studies have shown that class classification can be greatly improved by kernel Fisher discriminant analysis (KDA) technique. However, KDA only captures global geometrical structure and disregards local geometrical structure of the data. In this paper, we propose a new feature extraction algorithm, called locality preserving KDA (LPKDA) algorithm. LPKDA first casts KDA as a least squares problem in the kernel space and then explicitly incorporates the local geometrical structure information into the least squares problem via regularization technique. The fact that LPKDA can make full use of two kinds of discriminant information, global and local, makes it a more powerful discriminator. Experimental results on four image databases show that LPKDA outperforms other kernel-based algorithms.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Di Zhang AU - Jiazhong He AU - Yun Zhao PY - 2017 DA - 2017/01 TI - Kernel Fisher Discriminant Analysis with Locality Preserving for Feature Extraction and Recognition JO - International Journal of Computational Intelligence Systems SP - 1059 EP - 1071 VL - 6 IS - 6 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2013.816051 DO - https://doi.org/10.1080/18756891.2013.816051 ID - Zhang2017 ER -