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

DOI

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.

Copyright

© 2017, 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/).

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TY  - JOUR
AU  - Di Zhang
AU  - Jiazhong He
AU  - Yun Zhao
PY  - 2013
DA  - 2013/11/01
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  - 10.1080/18756891.2013.816051
ID  - Zhang2013
ER  -