International Journal of Computational Intelligence Systems

Volume 6, Issue 6, December 2013, Pages 1059 - 1071

Kernel Fisher Discriminant Analysis with Locality Preserving for Feature Extraction and Recognition

Authors
Di Zhang, Jiazhong He, Yun Zhao
Corresponding Author
Di Zhang
Available Online 9 January 2017.
DOI
https://doi.org/10.1080/18756891.2013.816051How to use a DOI?
Keywords
Kernel-based method, Fisher discriminant analysis, feature extraction, pattern classification
Abstract
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.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
6 - 6
Pages
1059 - 1071
Publication Date
2017/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2013.816051How to use a DOI?
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  -